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authorMichel Zou2019-01-08 19:34:11 +0100
committerMichel Zou2019-01-08 19:34:11 +0100
commit4a75591f31b4d04c24e2d23ff276ef1bdd3338b2 (patch)
tree85fcfb585086c88dd1ca123175bddd3c394450bd
parent4e155d7d2c6215da49a3b8a36542c04ec6b07d15 (diff)
downloadaur-4a75591f31b4d04c24e2d23ff276ef1bdd3338b2.tar.gz
3.0
Diffstat
-rw-r--r--.SRCINFO27
-rw-r--r--PKGBUILD24
-rw-r--r--r831.patch64
-rw-r--r--r833.patch85
-rw-r--r--r836.patch92
-rw-r--r--r837.patch191
-rw-r--r--r838.patch116
-rw-r--r--r839.patch5467
-rw-r--r--r840.patch206
-rw-r--r--r841.patch13
-rw-r--r--r845.patch1689
-rw-r--r--sundials4.patch26
12 files changed, 40 insertions, 7960 deletions
diff --git a/.SRCINFO b/.SRCINFO
index 6ec800dbb554..720b0ac94a3b 100644
--- a/.SRCINFO
+++ b/.SRCINFO
@@ -1,9 +1,7 @@
-# Generated by mksrcinfo v8
-# Wed May 16 13:43:51 UTC 2018
pkgbase = python-assimulo
pkgdesc = A package for solving ordinary differential equations and differential algebraic equations
- pkgver = 2.9
- pkgrel = 4
+ pkgver = 3.0
+ pkgrel = 1
url = http://www.jmodelica.org/assimulo
arch = i686
arch = x86_64
@@ -15,24 +13,9 @@ pkgbase = python-assimulo
makedepends = gcc-fortran
makedepends = sundials
makedepends = lapack
- source = https://pypi.python.org/packages/4c/c0/19a54949817204313efff9f83f1e4a247edebed0a1cc5a317a95d3f374ae/Assimulo-2.9.zip
- source = r831.patch
- source = r833.patch
- source = r836.patch
- source = r837.patch
- source = r838.patch
- source = r839.patch
- source = r840.patch
- source = r841.patch
- source = r845.patch
- md5sums = 3f28fd98011d2ec7a01703a1ef1dff45
- md5sums = SKIP
- md5sums = SKIP
- md5sums = SKIP
- md5sums = SKIP
- md5sums = SKIP
- md5sums = SKIP
- md5sums = SKIP
+ makedepends = subversion
+ source = svn+https://svn.jmodelica.org/assimulo/tags/Assimulo-3.0
+ source = sundials4.patch
md5sums = SKIP
md5sums = SKIP
diff --git a/PKGBUILD b/PKGBUILD
index 3c8a03efe092..5b0c6baca9d6 100644
--- a/PKGBUILD
+++ b/PKGBUILD
@@ -1,28 +1,22 @@
pkgbase=python-assimulo
pkgname=('python-assimulo' 'python2-assimulo')
-pkgver=2.9
-pkgrel=4
+pkgver=3.0
+pkgrel=1
pkgdesc="A package for solving ordinary differential equations and differential algebraic equations"
url="http://www.jmodelica.org/assimulo"
arch=('i686' 'x86_64')
license=('LGPL')
-makedepends=('python-setuptools' 'python2-setuptools' 'cython' 'cython2' 'gcc-fortran' 'sundials' 'lapack')
-source=("https://pypi.python.org/packages/4c/c0/19a54949817204313efff9f83f1e4a247edebed0a1cc5a317a95d3f374ae/Assimulo-2.9.zip" r831.patch r833.patch r836.patch r837.patch r838.patch r839.patch r840.patch r841.patch r845.patch)
-md5sums=('3f28fd98011d2ec7a01703a1ef1dff45' SKIP SKIP SKIP SKIP SKIP SKIP SKIP SKIP SKIP)
+makedepends=('python-setuptools' 'python2-setuptools' 'cython' 'cython2' 'gcc-fortran' 'sundials' 'lapack' 'subversion')
+# PyPI archive is missing .pyf files
+# source=("https://pypi.io/packages/source/A/Assimulo/Assimulo-${pkgver}.tar.gz")
+source=("svn+https://svn.jmodelica.org/assimulo/tags/Assimulo-${pkgver}" sundials4.patch)
+md5sums=('SKIP' SKIP )
prepare() {
cd "${srcdir}"/Assimulo-$pkgver
- # https://trac.jmodelica.org/assimulo/changeset/845
- patch -p0 -i "${srcdir}"/r831.patch
- patch -p0 -i "${srcdir}"/r833.patch
- patch -p0 -i "${srcdir}"/r836.patch
- patch -p0 -i "${srcdir}"/r837.patch
- patch -p0 -i "${srcdir}"/r838.patch
- patch -p0 -i "${srcdir}"/r839.patch
- patch -p0 -i "${srcdir}"/r840.patch
- patch -p0 -i "${srcdir}"/r841.patch
- patch -p0 -i "${srcdir}"/r845.patch
+ # try to build with sundials 4.0
+ patch -p0 -i "${srcdir}"/sundials4.patch
}
build() {
diff --git a/r831.patch b/r831.patch
deleted file mode 100644
index 1215207005ad..000000000000
--- a/r831.patch
+++ /dev/null
@@ -1,64 +0,0 @@
-Index: src/solvers/__init__.py
-===================================================================
---- assimulo/solvers/__init__.py (revision 830)
-+++ assimulo/solvers/__init__.py (revision 831)
-@@ -19,16 +19,50 @@
- "glimda","odepack","radar5","dasp3","odassl"]
-
- #Import all the solvers from the different modules
--from .euler import ExplicitEuler, ImplicitEuler
--from .radau5 import Radau5ODE, Radau5DAE, _Radau5ODE, _Radau5DAE
--from .sundials import IDA, CVode
--from .kinsol import KINSOL
--from .runge_kutta import RungeKutta34, RungeKutta4, Dopri5
--from .rosenbrock import RodasODE
--from .odassl import ODASSL
--from .odepack import LSODAR
--from .radar5 import Radar5ODE
- try:
-+ from .euler import ExplicitEuler
-+ from .euler import ImplicitEuler
-+except ImportError as ie:
-+ print("Could not find {}".format(ie.args[0][16:]))
-+try:
-+ from .radau5 import Radau5ODE
-+ from .radau5 import Radau5DAE
-+ from .radau5 import _Radau5ODE
-+ from .radau5 import _Radau5DAE
-+except ImportError as ie:
-+ print("Could not find {}".format(ie.args[0][16:]))
-+try:
-+ from .sundials import IDA
-+ from .sundials import CVode
-+except ImportError as ie:
-+ print("Could not find {}".format(ie.args[0][16:]))
-+try:
-+ from .kinsol import KINSOL
-+except ImportError as ie:
-+ print("Could not find {}".format(ie.args[0][16:]))
-+try:
-+ from .runge_kutta import RungeKutta34
-+ from .runge_kutta import RungeKutta4
-+ from .runge_kutta import Dopri5
-+except ImportError as ie:
-+ print("Could not find {}".format(ie.args[0][16:]))
-+try:
-+ from .rosenbrock import RodasODE
-+except ImportError as ie:
-+ print("Could not find {}".format(ie.args[0][16:]))
-+try:
-+ from .odassl import ODASSL
-+except ImportError as ie:
-+ print("Could not find {}".format(ie.args[0][16:]))
-+try:
-+ from .odepack import LSODAR
-+except ImportError as ie:
-+ print("Could not find {}".format(ie.args[0][16:]))
-+try:
-+ from .radar5 import Radar5ODE
-+except ImportError as ie:
-+ print("Could not find {}".format(ie.args[0][16:]))
-+try:
- from .dasp3 import DASP3ODE
- except ImportError as ie:
- print("Could not find {}".format(ie.args[0][16:]))
diff --git a/r833.patch b/r833.patch
deleted file mode 100644
index 7385443600c1..000000000000
--- a/r833.patch
+++ /dev/null
@@ -1,85 +0,0 @@
-Index: src/solvers/radar5.py
-===================================================================
---- assimulo/solvers/radar5.py (revision 832)
-+++ assimulo/solvers/radar5.py (revision 833)
-@@ -26,9 +26,11 @@
- from assimulo.explicit_ode import Explicit_ODE
- from assimulo.implicit_ode import Implicit_ODE
-
--from assimulo.lib import radar5
-+try:
-+ from assimulo.lib import radar5
-+except ImportError:
-+ print("Could not find RADAR5")
-
--
- class Radar_Exception(Exception):
- pass
-
-Index: src/solvers/__init__.py
-===================================================================
---- assimulo/solvers/__init__.py (revision 832)
-+++ assimulo/solvers/__init__.py (revision 833)
-@@ -23,7 +23,7 @@
- from .euler import ExplicitEuler
- from .euler import ImplicitEuler
- except ImportError as ie:
-- print("Could not find {}".format(ie.args[0][16:]))
-+ print("Could not find {}".format(ie.args[0].split("'")[1]))
- try:
- from .radau5 import Radau5ODE
- from .radau5 import Radau5DAE
-@@ -30,43 +30,43 @@
- from .radau5 import _Radau5ODE
- from .radau5 import _Radau5DAE
- except ImportError as ie:
-- print("Could not find {}".format(ie.args[0][16:]))
-+ print("Could not find {}".format(ie.args[0].split("'")[1]))
- try:
- from .sundials import IDA
- from .sundials import CVode
- except ImportError as ie:
-- print("Could not find {}".format(ie.args[0][16:]))
-+ print("Could not find {}".format(ie.args[0].split("'")[1]))
- try:
- from .kinsol import KINSOL
- except ImportError as ie:
-- print("Could not find {}".format(ie.args[0][16:]))
-+ print("Could not find {}".format(ie.args[0].split("'")[1]))
- try:
- from .runge_kutta import RungeKutta34
- from .runge_kutta import RungeKutta4
- from .runge_kutta import Dopri5
- except ImportError as ie:
-- print("Could not find {}".format(ie.args[0][16:]))
-+ print("Could not find {}".format(ie.args[0].split("'")[1]))
- try:
- from .rosenbrock import RodasODE
- except ImportError as ie:
-- print("Could not find {}".format(ie.args[0][16:]))
-+ print("Could not find {}".format(ie.args[0].split("'")[1]))
- try:
- from .odassl import ODASSL
- except ImportError as ie:
-- print("Could not find {}".format(ie.args[0][16:]))
-+ print("Could not find {}".format(ie.args[0].split("'")[1]))
- try:
- from .odepack import LSODAR
- except ImportError as ie:
-- print("Could not find {}".format(ie.args[0][16:]))
-+ print("Could not find {}".format(ie.args[0].split("'")[1]))
- try:
- from .radar5 import Radar5ODE
- except ImportError as ie:
-- print("Could not find {}".format(ie.args[0][16:]))
-+ print("Could not find {}".format(ie.args[0].split("'")[1]))
- try:
- from .dasp3 import DASP3ODE
- except ImportError as ie:
-- print("Could not find {}".format(ie.args[0][16:]))
-+ print("Could not find {}".format(ie.args[0].split("'")[1]))
- try:
- from .glimda import GLIMDA
- except ImportError as ie:
-- print("Could not find {}".format(ie.args[0][16:]))
-+ print("Could not find {}".format(ie.args[0].split("'")[1]))
diff --git a/r836.patch b/r836.patch
deleted file mode 100644
index b3cc0a96ba5d..000000000000
--- a/r836.patch
+++ /dev/null
@@ -1,92 +0,0 @@
-Index: src/lib/sundials_callbacks.pxi
-===================================================================
---- assimulo/lib/sundials_callbacks.pxi (revision 835)
-+++ assimulo/lib/sundials_callbacks.pxi (revision 836)
-@@ -103,7 +103,8 @@
- Sparse Jacobian function.
- """
- cdef ProblemData pData = <ProblemData>problem_data
-- cdef N.ndarray y = nv2arr(yv)
-+ #cdef N.ndarray y = nv2arr(yv)
-+ cdef N.ndarray y = pData.work_y
- cdef int i
- cdef int nnz = Jacobian.NNZ
- cdef int ret_nnz
-@@ -111,7 +112,8 @@
- cdef realtype* data = Jacobian.data
- cdef int* rowvals = Jacobian.rowvals
- cdef int* colptrs = Jacobian.colptrs
--
-+
-+ nv2arr_inplace(yv, y)
- """
- realtype *data;
- int *rowvals;
-@@ -160,8 +162,11 @@
- #cdef ndarray[realtype, ndim=2, mode='c'] jac #Used for return from the user function
- cdef realtype* col_i=DENSE_COL(Jacobian,0)
- #(<ndarray>pData.y).data = <realtype*>((<N_VectorContent_Serial>yv.content).data)
-- cdef N.ndarray y = nv2arr(yv)
-+ #cdef N.ndarray y = nv2arr(yv)
-+ cdef N.ndarray y = pData.work_y
- cdef int i,j
-+
-+ nv2arr_inplace(yv, y)
-
- if pData.dimSens>0: #Sensitivity activated
- p = realtype2arr(pData.p,pData.dimSens)
-@@ -358,11 +363,16 @@
- cdef N.ndarray[realtype, ndim=1, mode='c'] res #Used for return from the user function
- #(<ndarray>pData.y).data = <realtype*>((<N_VectorContent_Serial>yv.content).data)
- #(<ndarray>pData.yd).data = <realtype*>((<N_VectorContent_Serial>yvdot.content).data)
-- cdef N.ndarray y = nv2arr(yv)
-- cdef N.ndarray yd = nv2arr(yvdot)
-+ cdef N.ndarray y = pData.work_y
-+ cdef N.ndarray yd = pData.work_yd
-+ # cdef N.ndarray y = nv2arr(yv)
-+ # cdef N.ndarray yd = nv2arr(yvdot)
- cdef realtype* resptr=(<N_VectorContent_Serial>residual.content).data
- cdef int i
-
-+ nv2arr_inplace(yv, y)
-+ nv2arr_inplace(yvdot, yd)
-+
- if pData.dimSens!=0: #SENSITIVITY
- p = realtype2arr(pData.p,pData.dimSens)
- try:
-@@ -411,10 +421,15 @@
- cdef realtype* col_i=DENSE_COL(Jacobian,0)
- #(<ndarray>pData.y).data = <realtype*>((<N_VectorContent_Serial>yv.content).data)
- #(<ndarray>pData.yd).data = <realtype*>((<N_VectorContent_Serial>yvdot.content).data)
-- cdef N.ndarray y = nv2arr(yv)
-- cdef N.ndarray yd = nv2arr(yvdot)
-+ cdef N.ndarray y = pData.work_y
-+ cdef N.ndarray yd = pData.work_yd
-+ #cdef N.ndarray y = nv2arr(yv)
-+ #cdef N.ndarray yd = nv2arr(yvdot)
- cdef int i,j
-
-+ nv2arr_inplace(yv, y)
-+ nv2arr_inplace(yvdot, yd)
-+
- if pData.dimSens!=0: #SENSITIVITY
- p = realtype2arr(pData.p,pData.dimSens)
- try:
-@@ -461,10 +476,15 @@
- cdef N.ndarray[realtype, ndim=1, mode='c'] root #Used for return from the user function
- #(<ndarray>pData.y).data = <realtype*>((<N_VectorContent_Serial>yv.content).data)
- #(<ndarray>pData.yd).data = <realtype*>((<N_VectorContent_Serial>yvdot.content).data)
-- cdef N.ndarray y = nv2arr(yv)
-- cdef N.ndarray yd = nv2arr(yvdot)
-+ cdef N.ndarray y = pData.work_y
-+ cdef N.ndarray yd = pData.work_yd
-+ #cdef N.ndarray y = nv2arr(yv)
-+ #cdef N.ndarray yd = nv2arr(yvdot)
- cdef int i
-
-+ nv2arr_inplace(yv, y)
-+ nv2arr_inplace(yvdot, yd)
-+
- try:
- if pData.sw != NULL:
- root=(<object>pData.ROOT)(t,y,yd,<list>pData.sw) #Call to the Python root function
diff --git a/r837.patch b/r837.patch
deleted file mode 100644
index e5fdd761d3a3..000000000000
--- a/r837.patch
+++ /dev/null
@@ -1,191 +0,0 @@
-Index: src/implicit_ode.pyx
-===================================================================
---- assimulo/implicit_ode.pyx (revision 836)
-+++ assimulo/implicit_ode.pyx (revision 837)
-@@ -26,7 +26,7 @@
- cimport numpy as N
-
- from exception import *
--from time import clock, time
-+from timeit import default_timer as timer
- import warnings
-
- realtype = N.float
-@@ -187,7 +187,7 @@
- output_index = 0
-
- self.time_limit_activated = 1 if self.time_limit > 0 else 0
-- self.time_integration_start = time()
-+ self.time_integration_start = timer()
-
- while (flag == ID_COMPLETE and tevent == tfinal) is False and (self.t-eps > tfinal) if backward else (self.t+eps < tfinal):
-
-@@ -203,7 +203,7 @@
-
- #Initialize the clock, enabling storing elapsed time for each step
- if REPORT_CONTINUOUSLY and self.options["clock_step"]:
-- self.clock_start = clock()
-+ self.clock_start = timer()
-
- [flag, tlist, ylist, ydlist] = self.integrate(self.t, self.y, self.yd, tevent, opts)
-
-@@ -274,16 +274,16 @@
-
- #Store the elapsed time for a single step
- if self.options["clock_step"]:
-- self.elapsed_step_time = clock() - self.clock_start
-- self.clock_start = clock()
-+ self.elapsed_step_time = timer() - self.clock_start
-+ self.clock_start = timer()
-
- #Check elapsed timed
- if self.time_limit_activated:
-- if self.time_limit-(time()-self.time_integration_start) < 0.0:
-+ if self.time_limit-(timer()-self.time_integration_start) < 0.0:
- raise TimeLimitExceeded("The time limit was exceeded at integration time %.8E."%self.t)
-
- if self.display_progress:
-- if (time() - self.time_integration_start) > self.display_counter*10:
-+ if (timer() - self.time_integration_start) > self.display_counter*10:
- self.display_counter += 1
-
- sys.stdout.write(" Integrator time: %e" % self.t)
-Index: src/ode.pyx
-===================================================================
---- assimulo/ode.pyx (revision 836)
-+++ assimulo/ode.pyx (revision 837)
-@@ -17,7 +17,7 @@
-
- import numpy as N
- cimport numpy as N
--import time
-+from timeit import default_timer as timer
- import pylab as P
-
- import itertools
-@@ -283,13 +283,13 @@
- self.initialize()
-
- #Start of simulation, start the clock
-- time_start = time.clock()
-+ time_start = timer()
-
- #Start the simulation
- self._simulate(t0, tfinal, output_list, REPORT_CONTINUOUSLY, INTERPOLATE_OUTPUT, TIME_EVENT)
-
- #End of simulation, stop the clock
-- time_stop = time.clock()
-+ time_stop = timer()
-
- #Simulation complete, call finalize
- self.finalize()
-Index: src/kinsol.py
-===================================================================
---- assimulo/kinsol.py (revision 836)
-+++ assimulo/kinsol.py (revision 837)
-@@ -26,7 +26,7 @@
- import pylab as P
- import operator as O
- import re
--import time
-+from timeit import default_timer as timer
- from assimulo.problem_algebraic import *
-
- class KINSOL_Exception(Exception):
-@@ -290,9 +290,9 @@
- self.solver.KINSOL_init(self.func,self.x0,self.dim,jac,self.constraints,self.use_sparse,self.verbosity,self.norm_of_res,self.reg_param,self.fscale)
- else:
- self.solver.KINSOL_init(self.func,self.x0,self.dim,jac,self.constraints,self.use_sparse,self.verbosity,self.norm_of_res,self.reg_param,None)
-- start = time.clock()
-+ start = timer()
- res = self.solver.KINSOL_solve(not self._use_ls)
-- stop = time.clock()
-+ stop = timer()
- self.exec_time += (stop - start)
- solved = True
- except KINError as error:
-Index: src/explicit_ode.pyx
-===================================================================
---- assimulo/explicit_ode.pyx (revision 836)
-+++ assimulo/explicit_ode.pyx (revision 837)
-@@ -25,7 +25,7 @@
- cimport numpy as N
-
- from exception import *
--from time import clock, time
-+from timeit import default_timer as timer
-
- include "constants.pxi" #Includes the constants (textual include)
-
-@@ -170,7 +170,7 @@
- output_index = 0
-
- self.time_limit_activated = 1 if self.time_limit > 0 else 0
-- self.time_integration_start = time()
-+ self.time_integration_start = timer()
-
- while (flag == ID_COMPLETE and tevent == tfinal) is False and (self.t-eps > tfinal) if backward else (self.t+eps < tfinal):
-
-@@ -183,7 +183,7 @@
-
- #Initialize the clock, enabling storing elapsed time for each step
- if REPORT_CONTINUOUSLY and self.options["clock_step"]:
-- self.clock_start = clock()
-+ self.clock_start = timer()
-
- flag, tlist, ylist = self.integrate(self.t, self.y, tevent, opts)
-
-@@ -248,12 +248,12 @@
-
- #Store the elapsed time for a single step
- if self.options["clock_step"]:
-- self.elapsed_step_time = clock() - self.clock_start
-- self.clock_start = clock()
-+ self.elapsed_step_time = timer() - self.clock_start
-+ self.clock_start = timer()
-
- #Check elapsed timed
- if self.time_limit_activated:
-- if self.time_limit-(time()-self.time_integration_start) < 0.0:
-+ if self.time_limit-(timer()-self.time_integration_start) < 0.0:
- raise TimeLimitExceeded("The time limit was exceeded at integration time %.8E."%self.t)
-
- self.chattering_clear_counter += 1
-@@ -262,7 +262,7 @@
- self.chattering_ok_print = 1
-
- if self.display_progress:
-- if (time() - self.time_integration_start) > self.display_counter*10:
-+ if (timer() - self.time_integration_start) > self.display_counter*10:
- self.display_counter += 1
-
- sys.stdout.write(" Integrator time: %e" % self.t)
-Index: src/algebraic.pyx
-===================================================================
---- assimulo/algebraic.pyx (revision 836)
-+++ assimulo/algebraic.pyx (revision 837)
-@@ -17,7 +17,7 @@
-
- import numpy as N
- cimport numpy as N
--import time
-+from timeit import default_timer as timer
- import pylab as P
-
- import itertools
-@@ -102,13 +102,13 @@
- self.initialize()
-
- #Start of simulation, start the clock
-- time_start = time.clock()
-+ time_start = timer()
-
- #Start the simulation
- self.y = self._solve(y)
-
- #End of simulation, stop the clock
-- time_stop = time.clock()
-+ time_stop = timer()
-
- #Simulation complete, call finalize
- self.finalize()
diff --git a/r838.patch b/r838.patch
deleted file mode 100644
index 89104386f156..000000000000
--- a/r838.patch
+++ /dev/null
@@ -1,116 +0,0 @@
-Index: src/solvers/glimda.py
-===================================================================
---- assimulo/solvers/glimda.py (revision 837)
-+++ assimulo/solvers/glimda.py (revision 838)
-@@ -16,6 +16,7 @@
- # along with this program. If not, see <http://www.gnu.org/licenses/>.
-
- import numpy as N
-+import sys
-
- from assimulo.exception import *
- from assimulo.ode import *
-@@ -25,7 +26,7 @@
- try:
- from assimulo.lib.glimda import glimda
- except ImportError:
-- print("Could not find GLIMDA")
-+ sys.stderr.write("Could not find GLIMDA.\n")
-
- class GLIMDA(Implicit_ODE):
- """
-Index: src/solvers/__init__.py
-===================================================================
---- assimulo/solvers/__init__.py (revision 837)
-+++ assimulo/solvers/__init__.py (revision 838)
-@@ -18,12 +18,14 @@
- __all__ = ["euler","radau5","sundials","runge_kutta","rosenbrock",
- "glimda","odepack","radar5","dasp3","odassl"]
-
-+import sys
-+
- #Import all the solvers from the different modules
- try:
- from .euler import ExplicitEuler
- from .euler import ImplicitEuler
- except ImportError as ie:
-- print("Could not find {}".format(ie.args[0].split("'")[1]))
-+ sys.stderr.write("Could not find {}\n".format(ie.args[0].split("'")[1]))
- try:
- from .radau5 import Radau5ODE
- from .radau5 import Radau5DAE
-@@ -30,43 +32,43 @@
- from .radau5 import _Radau5ODE
- from .radau5 import _Radau5DAE
- except ImportError as ie:
-- print("Could not find {}".format(ie.args[0].split("'")[1]))
-+ sys.stderr.write("Could not find {}\n".format(ie.args[0].split("'")[1]))
- try:
- from .sundials import IDA
- from .sundials import CVode
- except ImportError as ie:
-- print("Could not find {}".format(ie.args[0].split("'")[1]))
-+ sys.stderr.write("Could not find {}\n".format(ie.args[0].split("'")[1]))
- try:
- from .kinsol import KINSOL
- except ImportError as ie:
-- print("Could not find {}".format(ie.args[0].split("'")[1]))
-+ sys.stderr.write("Could not find {}\n".format(ie.args[0].split("'")[1]))
- try:
- from .runge_kutta import RungeKutta34
- from .runge_kutta import RungeKutta4
- from .runge_kutta import Dopri5
- except ImportError as ie:
-- print("Could not find {}".format(ie.args[0].split("'")[1]))
-+ sys.stderr.write("Could not find {}\n".format(ie.args[0].split("'")[1]))
- try:
- from .rosenbrock import RodasODE
- except ImportError as ie:
-- print("Could not find {}".format(ie.args[0].split("'")[1]))
-+ sys.stderr.write("Could not find {}\n".format(ie.args[0].split("'")[1]))
- try:
- from .odassl import ODASSL
- except ImportError as ie:
-- print("Could not find {}".format(ie.args[0].split("'")[1]))
-+ sys.stderr.write("Could not find {}\n".format(ie.args[0].split("'")[1]))
- try:
- from .odepack import LSODAR
- except ImportError as ie:
-- print("Could not find {}".format(ie.args[0].split("'")[1]))
-+ sys.stderr.write("Could not find {}\n".format(ie.args[0].split("'")[1]))
- try:
- from .radar5 import Radar5ODE
- except ImportError as ie:
-- print("Could not find {}".format(ie.args[0].split("'")[1]))
-+ sys.stderr.write("Could not find {}\n".format(ie.args[0].split("'")[1]))
- try:
- from .dasp3 import DASP3ODE
- except ImportError as ie:
-- print("Could not find {}".format(ie.args[0].split("'")[1]))
-+ sys.stderr.write("Could not find {}\n".format(ie.args[0].split("'")[1]))
- try:
- from .glimda import GLIMDA
- except ImportError as ie:
-- print("Could not find {}".format(ie.args[0].split("'")[1]))
-+ sys.stderr.write("Could not find {}\n".format(ie.args[0].split("'")[1]))
-Index: src/solvers/odepack.py
-===================================================================
---- assimulo/solvers/odepack.py (revision 837)
-+++ assimulo/solvers/odepack.py (revision 838)
-@@ -17,6 +17,7 @@
-
- import numpy as N
- import scipy.linalg as Sc
-+import sys
- from assimulo.exception import *
- from assimulo.ode import *
- import logging
-@@ -27,7 +28,7 @@
- from assimulo.lib.odepack import dlsodar, dcfode, dintdy
- from assimulo.lib.odepack import set_lsod_common, get_lsod_common
- except ImportError:
-- print("Could not find ODEPACK functions")
-+ sys.stderr.write("Could not find ODEPACK functions.\n")
-
- # Real work array
- class common_like(object):
diff --git a/r839.patch b/r839.patch
deleted file mode 100644
index 9fde401d3862..000000000000
--- a/r839.patch
+++ /dev/null
@@ -1,5467 +0,0 @@
-Index: setup.py
-===================================================================
---- setup.py (revision 838)
-+++ setup.py (revision 839)
-@@ -387,39 +387,7 @@
- if self.platform == "mac":
- el.extra_compile_args += ["-Wno-error=return-type"]
- el.extra_compile_args += self.flag_32bit + self.extra_c_flags
--
-- if self.with_SUNDIALS:
-- cordir_KINSOL_wSLU = os.path.join(self.assimulo_lib,'sundials_kinsol_core_wSLU.pyx')
-- cordir_KINSOL = os.path.join(self.assimulo_lib,'sundials_kinsol_core.pyx')
--
-- cordir_KINSOL_jmod_wSLU = os.path.join(self.assimulo_lib,'kinsol_jmod_wSLU.c')
-- cordir_KINSOL_jmod = os.path.join(self.assimulo_lib,'kinsol_jmod.c')
--
-- cordir_kinpinv = os.path.join(self.assimulo_lib,'kinpinv.c')
-- cordir_kinslug = os.path.join(self.assimulo_lib,'kinslug.c')
-- cordir_reg_routines = os.path.join(self.assimulo_lib,'reg_routines.c')
-- if self.with_SLU:
-- ext_list = ext_list + cythonize([cordir_KINSOL_wSLU], include_path=[".","assimulo",self.assimulo_lib])
-- ext_list[-1].sources += [cordir_KINSOL_jmod_wSLU,cordir_kinpinv,cordir_kinslug,cordir_reg_routines]
-- ext_list[-1].include_dirs = [np.get_include(), self.SLUincdir, self.incdirs]
-- ext_list[-1].library_dirs = [self.libdirs,self.SLUlibdir,self.BLASdir]
-- ext_list[-1].libraries = ["sundials_kinsol", "sundials_nvecserial", "superlu_4.1",self.BLASname,'gfortran']
-- else:
-- ext_list = ext_list + cythonize([cordir_KINSOL])#, include_path=[".","assimulo",self.assimulo_lib])
-- ext_list[-1].sources += [cordir_KINSOL_jmod,cordir_kinpinv]
-- ext_list[-1].include_dirs = [np.get_include(), self.incdirs]
-- ext_list[-1].library_dirs = [self.libdirs]
-- ext_list[-1].libraries = ["sundials_kinsol", "sundials_nvecserial"]
-- if self.SUNDIALS_version > (2,5,0):
-- ext_list[-1].define_macros.append(("SUNDIALS_26", 1))
-- if self.debug_flag:
-- ext_list[-1].extra_compile_args = ["-g", "-fno-strict-aliasing"]
-- else:
-- ext_list[-1].extra_compile_args = ["-O2", "-fno-strict-aliasing"]
-- if self.platform == "mac":
-- ext_list[-1].extra_compile_args += ["-Wno-error=return-type"]
-- ext_list[-1].extra_compile_args += self.flag_32bit + self.extra_c_flags
--
-+
- for el in ext_list:
- if self.is_python3:
- el.cython_directives = {"language_level": 3}
-Index: src/kinsol.py
-===================================================================
---- assimulo/kinsol.py (revision 838)
-+++ assimulo/kinsol.py (nonexistent)
-@@ -1,350 +0,0 @@
--#!/usr/bin/env python
--# -*- coding: utf-8 -*-
--
--# Copyright (C) 2010 Modelon AB
--#
--# This program is free software: you can redistribute it and/or modify
--# it under the terms of the GNU Lesser General Public License as published by
--# the Free Software Foundation, version 3 of the License.
--#
--# This program is distributed in the hope that it will be useful,
--# but WITHOUT ANY WARRANTY; without even the implied warranty of
--# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
--# GNU Lesser General Public License for more details.
--#
--# You should have received a copy of the GNU Lesser General Public License
--# along with this program. If not, see <http://www.gnu.org/licenses/>.
--try:
-- from .lib.sundials_kinsol_core_wSLU import KINSOL_wrap, KINError
--except:
-- from .lib.sundials_kinsol_core import KINSOL_wrap, KINError
--
--from scipy.linalg import pinv2
--from scipy.optimize import fminbound
--from numpy.linalg import solve
--import numpy as N
--import pylab as P
--import operator as O
--import re
--from timeit import default_timer as timer
--from assimulo.problem_algebraic import *
--
--class KINSOL_Exception(Exception):
-- pass
--
--
--class KINSOL:
--
-- def __init__(self,problem):
-- """
-- Create the solver
--
-- Parameters::
-- problem--
-- instance of ProblemAlgebraic found in problem_algebraic.py
-- """
--
-- self.solver = KINSOL_wrap()
--
-- # extract info from problem
-- self.problem = problem
-- if hasattr(self.problem,'_x0'):
-- try:
-- _x0 = self.problem.get_x0()
-- except ProblemAlg_Exception:
-- raise KINSOL_Exception("Problem has not implemented method 'get_x0'")
-- else:
-- raise KINSOL_Exception("Problem has no instance '_x0'")
--
-- # calculate dimension
-- try:
-- if isinstance(_x0, int) or isinstance(_x0, float):
-- self.x0 = [_x0]
-- else:
-- self.x0 = _x0
-- self.dim = len([N.array(self.x0, dtype=float)][0])
-- except ValueError:
-- raise KINSOL_Exception("Initial guess must be a Numpy.array with either ints or floats.")
--
-- # check for functions and test them
-- try:
-- tmp = self.problem.f(self.x0)
-- self.norm_of_res = 10000*N.linalg.norm(self.x0)
-- #self.norm_of_res = 1e20
-- self.func = self.problem.f
-- except ProblemAlg_Exception:
-- raise KINSOL_Exception("Problem has not implemented method 'f'")
-- except IndexError:
-- raise KINSOL_Exception("Problem has mismatching dimensions of f and initial guess")
--
-- # Calculate 'stupid' scaling
-- res = self.problem.f(self.x0)
-- self.fscale = N.ones(self.dim)
--
-- for i,val in zip(N.arange(self.dim),res):
-- if val > 10:
-- self.fscale[i] = 1.0/val
--
-- # check for constraints and test them
-- broken_constraints = []
-- if hasattr(self.problem, 'get_constraints'):
-- self.constraints = self.problem.get_constraints()
--
-- if self.constraints is not None:
-- # test if constraints are of correct type
-- if type(self.constraints).__name__ != 'ndarray':
-- raise KINSOL_Exception("Constraints must be of type numpy.ndarray")
--
-- if len(self.constraints) != len(self.x0):
-- raise KINSOL_Exception("Constraints must have same length as x0")
-- # Test if initial guess x0 is consistant with constraints
-- for c,xi,i in zip(self.constraints,self.x0,N.arange(0,self.x0.__len__())):
-- if re.search('float',type(c).__name__) is None:
-- print("Type problem with: ", c, type(c).__name__)
-- raise KINSOL_Exception("Constraints must contain floats.")
-- if abs(c) > 2:
-- raise KINSOL_Exception("Entries in constraint vector must be between -2 and 2, see documentation.")
--
-- if c != 0.0:
-- if c == 1.0:
-- if xi < 0.0:
-- broken_constraints.append(i)
-- elif c == 2.0:
-- if xi <= 0.0:
-- broken_constraints.append(i)
-- elif c == -1.0:
-- if xi > 0.0:
-- broken_constraints.append(i)
-- elif c == -2.0:
-- if xi >= 0.0:
-- broken_constraints.append(i)
-- else:
-- raise KINSOL_Exception("Constraint vector contains illegal elements.")
--
-- else:
-- self.constraints = None
--
-- if broken_constraints != []:
-- print("Variables breaking initial constraint: ")
-- for i in broken_constraints:
-- self.problem.print_var_info(i)
--
-- raise KINSOL_Exception("Initial guess does not fulfill applied constraints.")
--
--
-- if hasattr(self.problem, 'check_constraints'):
-- self.check_with_model = True
-- else:
-- self.check_with_model = False
--
-- self._use_jac = True
-- self.reg_count = 0
-- self.lin_count = 0
-- self.verbosity = 0
-- self.max_reg = 2.0
-- self.use_sparse = False
-- self.reg_param = 0.0
-- self.exec_time = 0
-- self._use_ls = True
-- self._use_fscale = False
--
-- def set_jac_usage(self,use_jac):
-- """
-- Set whether to use the jacobian supplied by the model
-- or if we are to calculate it numericaly
--
-- Parameters::
--
-- use_jac --
-- Boolean set to True if the jacobian is to be
-- supplied by the model
--
-- """
-- if type(use_jac).__name__ == 'bool':
-- self._use_jac = use_jac
-- else:
-- raise KINSOL_Exception("The variable sent to 'set_jac_usage' must be a boolean.")
--
-- def use_LineSearch(self,use_ls):
-- """
-- Set whether the solver starts using the Linesearch Algorithm
-- or not
--
-- Parameters::
--
-- use_ls --
-- Boolean set to True if LineSearch is to
-- be used.
--
-- """
-- if type(use_ls).__name__ == 'bool':
-- self._use_ls = use_ls
-- else:
-- raise KINSOL_Exception("The variable sent to 'use_LineSearch' must be a boolean.")
--
-- def use_fscale(self,use_fscale):
-- """
-- Set whether the solver starts using the Linesearch Algorithm
-- or not
--
-- Parameters::
--
-- use_ls --
-- Boolean set to True if LineSearch is to
-- be used.
--
-- """
-- if type(use_fscale).__name__ == 'bool':
-- self._use_fscale = use_fscale
-- else:
-- raise KINSOL_Exception("The variable sent to 'use_fscale' must be a boolean.")
--
--
-- def set_verbosity(self,verbosity):
-- """
-- Method used to set the verbosity of KINSOL
--
-- Parameters::
--
-- verbosity --
-- Integer set to one of 0,1,2,3 where 0 is no info and 3 the
-- most info. For more information please see the documentation for KINSOL.
-- """
-- type_name = type(verbosity).__name__
-- if re.search('int',type_name) is not None:
--
-- # It is an integer, tes bounds
-- if verbosity < 4 and verbosity > -1:
-- self.verbosity = verbosity
-- else:
-- raise KINSOL_Exception("The variable sent to 'set_verbosity' must be either 0, 1, 2 or 3.")
-- else:
-- raise KINSOL_Exception("The variable sent to 'set_verbosity' must be an integer.")
--
-- def set_sparsity(self,use_sparse):
-- """
-- Method used to set if the problem should be treated as sparse by
-- the linear solver in KINSOL. If the problem supplied has not implemented
-- a method sparse_jac an exception will be thrown
--
-- Parameters::
--
-- use_sparse --
-- Boolean set to True if the problem is to be treated as
-- sparse and False otherwise.
--
-- """
--
-- if hasattr(self.problem,'sparse_jac'):
-- self.use_sparse = use_sparse
-- else:
-- raise KINSOL_Exception("The problem must have implemented a method 'sparse_jac' for sparsity to by used.")
--
-- def set_reg_param(self,reg_param):
-- """
-- Method used to set the regularization parameter.
-- If set to zero the parameter will be set by the solver.
--
-- Parameters::
--
-- reg_param --
-- Float larger or equal to zero.
--
-- """
--
-- type_name = type(reg_param).__name__
-- if re.search('float',type_name) is not None:
-- if reg_param < 0:
-- raise KINSOL_Exception("Value sent to set_reg_param must be equal to or larger than zero.")
-- else:
-- self.reg_param = reg_param
-- else:
-- raise KINSOL_Exception("The variable sent to 'set_reg_param' must be a float.")
--
-- def solve(self):
-- """
-- Function called when solving function rhs_fct
--
-- """
-- # check for jacobian and set it if present and to be used
-- if self.use_sparse:
-- if self._use_jac and hasattr(self.problem,'sparse_jac'):
-- jac = self.problem.sparse_jac
-- else:
-- jac = None
-- else:
-- if self._use_jac and hasattr(self.problem,'jac'):
-- jac = self.problem.jac
-- else:
-- jac = None
--
-- # Initialize solver and solve
--
-- solved = False
-- local_min = False
--
-- res = N.zeros(self.x0.__len__())
-- while (not solved) and self.reg_count < 2:
-- try:
-- if self._use_fscale:
-- self.solver.KINSOL_init(self.func,self.x0,self.dim,jac,self.constraints,self.use_sparse,self.verbosity,self.norm_of_res,self.reg_param,self.fscale)
-- else:
-- self.solver.KINSOL_init(self.func,self.x0,self.dim,jac,self.constraints,self.use_sparse,self.verbosity,self.norm_of_res,self.reg_param,None)
-- start = timer()
-- res = self.solver.KINSOL_solve(not self._use_ls)
-- stop = timer()
-- self.exec_time += (stop - start)
-- solved = True
-- except KINError as error:
-- if error.value == 42:
-- # Try the heuristic
-- if hasattr(self.problem, 'get_heuristic_x0'):
-- print("----------------------------------------------------")
-- print(" Solver stuck with zero step-length.")
-- print("----------------------------------------------------")
-- print("The following variables have start value zero")
-- print("and min set to zero causing the zero step-lenght.")
-- print("These settings are either set by default or by user.")
-- print("")
--
-- self.x0 = self.problem.get_heuristic_x0()
-- self.reg_count += 1
--
-- print("")
-- print("This setting (start and min to zero) can often")
-- print("cause problem when initializing the system. ")
-- print("")
-- print("To avoid this the above variables have")
-- print("their start attributes reset to one.")
-- print("")
-- print("Trying to solve the system again...")
-- else:
-- raise KINSOL_Exception("Regularization failed due to constraints, tried getting heuristic initial guess but failed.")
--
--
-- elif (error.value == 2):
-- print("---------------------------------------------------------")
-- print("")
-- print(" !!! WARNING !!!")
-- print("")
-- print(" KINSOL has returned a result but the algorithm has converged")
-- print(" to a local minima, the initial values are NOT consistant!")
-- print("")
-- print("---------------------------------------------------------")
-- solved = True
-- local_min = True
-- else:
-- # Other error, send onward as exception
-- self.problem.check_constraints(res)
-- raise KINSOL_Exception(error.msg[error.value])
--
-- if not solved:
-- self.solver.Free_KINSOL()
-- raise KINSOL_Exception("Algorithm exited solution loop without finding a solution, please contact Assimulo support.")
--
-- if self.check_with_model:
-- self.problem.check_constraints(res)
-- if not local_min:
-- print("Problem sent to KINSOL solved.")
--
-- return res
-Index: src/lib/sundials_kinsol_core_wSLU.pxi
-===================================================================
---- assimulo/lib/sundials_kinsol_core_wSLU.pxi (revision 838)
-+++ assimulo/lib/sundials_kinsol_core_wSLU.pxi (nonexistent)
-@@ -1,293 +0,0 @@
--#!/usr/bin/env python
--# -*- coding: utf-8 -*-
--
--# Copyright (C) 2010 Modelon AB
--#
--# This program is free software: you can redistribute it and/or modify
--# it under the terms of the GNU Lesser General Public License as published by
--# the Free Software Foundation, version 3 of the License.
--#
--# This program is distributed in the hope that it will be useful,
--# but WITHOUT ANY WARRANTY; without even the implied warranty of
--# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
--# GNU Lesser General Public License for more details.
--#
--# You should have received a copy of the GNU Lesser General Public License
--# along with this program. If not, see <http://www.gnu.org/licenses/>.
--
--"""
--Cython Wrapper for interfacing Python with KINSOL (Sundials Version 2.4.0)
--Johan Ylikiiskilä Modelon AB
--
--see also Jon Olav Vik:
--http://codespeak.net/pipermail/cython-dev/2009-June/005947.html
--
--"""
--
--import numpy as np
--from numpy cimport NPY_DOUBLE, npy_intp, NPY_INT
--
--#==============================================
--#External definitions from numpy headers
--#==============================================
--cdef extern from "numpy/arrayobject.h":
--
-- ctypedef int npy_intp
--
-- ctypedef extern class numpy.dtype [object PyArray_Descr]:
-- cdef int type_num, elsize, alignment
-- cdef char type, kind, byteorder, hasobject
-- cdef object fields, typeobj
--
-- ctypedef int intp
-- ctypedef extern class numpy.ndarray [object PyArrayObject]:
-- cdef char *data
-- cdef int nd
-- cdef intp *dimensions
-- cdef intp *strides
-- cdef int flags
--
-- cdef object PyArray_SimpleNew(int nd, npy_intp* dims, int typenum)
-- cdef object PyArray_SimpleNewFromData(int nd, npy_intp *dims,
-- int typenum, void *data)
-- void import_array()
-- void* PyArray_GetPtr(ndarray aobj, npy_intp* ind)
-- void *PyArray_DATA(ndarray aobj)
--
--import_array()
--
--#==============================================
--#External definitions from SuperLU headers
--#==============================================
--cdef extern from "slu_ddefs.h":
-- ctypedef int int_t
--
-- ctypedef enum Stype_t:
-- SLU_NC
-- ctypedef enum Dtype_t:
-- SLU_D
-- ctypedef enum Mtype_t:
-- SLU_GE
-- ctypedef struct SuperMatrix:
-- Stype_t Stype
-- Dtype_t Dtype
-- Mtype_t Mtype
-- int_t nrow
-- int_t ncol
-- void *Store
-- void dCreate_CompCol_Matrix(SuperMatrix *, int, int, int, double *,int *, int *, Stype_t, Dtype_t, Mtype_t)
--#==============================================
--# C headers
--#==============================================
--cdef extern from "string.h":
-- void *memcpy(void *s1, void *s2, int n)
--cdef extern from "stdio.h":
-- int printf (char *__format, ...)
--cdef extern from "stdlib.h":
-- ctypedef unsigned long size_t
-- void *calloc(size_t nelem, size_t elsize)
--
--#==============================================
--#External definitions from Sundials headers
--#==============================================
--
--# import data types
--# basic data types
--cdef extern from "sundials/sundials_types.h":
-- ctypedef double realtype
-- ctypedef bint booleantype
--
--# sundials handling of vectors, so far only serial,
--# parallel to be implemented later?
--cdef extern from "sundials/sundials_nvector.h":
-- cdef struct _generic_N_Vector:
-- void* content
-- ctypedef _generic_N_Vector* N_Vector
--
--cdef extern from "nvector/nvector_serial.h":
-- cdef struct _N_VectorContent_Serial:
-- long int length
-- realtype* data
-- ctypedef _N_VectorContent_Serial* N_VectorContent_Serial
-- N_Vector N_VNew_Serial(long int vec_length)
-- void N_VDestroy_Serial(N_Vector v)
-- void N_VPrint_Serial(N_Vector v)
-- cdef N_Vector N_VMake_Serial(long int vec_length, realtype *v_data)
-- N_Vector *N_VCloneVectorArray_Serial(int count, N_Vector w)
-- N_Vector *N_VCloneVectorArrayEmpty_Serial(int count, N_Vector w)
-- void N_VSetArrayPointer_Serial(realtype *v_data, N_Vector v)
-- void N_VConst_Serial(realtype c, N_Vector z)
--
--#Struct for handling the Jacobian data
--cdef extern from "sundials/sundials_direct.h":
-- cdef struct _DlsMat:
-- int type
-- int M
-- int N
-- int ldim
-- int mu
-- int ml
-- int s_mu
-- realtype *data
-- int ldata
-- realtype **cols
-- ctypedef _DlsMat* DlsMat
-- cdef realtype* DENSE_COL(DlsMat A, int j)
--
--# KINSOL functions and routines
--cdef extern from "kinsol/kinsol.h":
-- # user defined functions
-- ctypedef int (*KINSysFn)(N_Vector uu, N_Vector fval, void *user_data )
-- ctypedef void (*KINErrHandlerFn)(int error_code, char *module, char *function, char *msg, void *user_data)
-- ctypedef void (*KINInfoHandlerFn)(char *module, char *function, char *msg, void *user_data)
-- # initialization routines
-- void *KINCreate()
-- int KINInit(void *kinmem, KINSysFn func, N_Vector tmpl)
--
-- # optional input spec. functions,
-- # for specificationsdocumentation cf. kinsol.h line 218-449
-- int KINSetErrHandlerFn(void *kinmem, KINErrHandlerFn ehfun, void *eh_data)
-- int KINSetInfoHandlerFn(void *kinmem, KINInfoHandlerFn ihfun, void *ih_data)
-- int KINSetUserData(void *kinmem, void *user_data)
-- int KINSetPrintLevel(void *kinmemm, int printfl)
-- int KINSetNumMaxIters(void *kinmem, long int mxiter)
-- int KINSetNoInitSetup(void *kinmem, booleantype noInitSetup)
-- int KINSetNoResMon(void *kinmem, booleantype noNNIResMon)
-- int KINSetMaxSetupCalls(void *kinmem, long int msbset)
-- int KINSetMaxSubSetupCalls(void *kinmem, long int msbsetsub)
-- int KINSetEtaForm(void *kinmem, int etachoice)
-- int KINSetEtaConstValue(void *kinmem, realtype eta)
-- int KINSetEtaParams(void *kinmem, realtype egamma, realtype ealpha)
-- int KINSetResMonParams(void *kinmem, realtype omegamin, realtype omegamax)
-- int KINSetResMonConstValue(void *kinmem, realtype omegaconst)
-- int KINSetNoMinEps(void *kinmem, booleantype noMinEps)
-- int KINSetMaxNewtonStep(void *kinmem, realtype mxnewtstep)
-- int KINSetMaxBetaFails(void *kinmem, long int mxnbcf)
-- int KINSetRelErrFunc(void *kinmem, realtype relfunc)
-- int KINSetFuncNormTol(void *kinmem, realtype fnormtol)
-- int KINSetScaledStepTol(void *kinmem, realtype scsteptol)
-- int KINSetConstraints(void *kinmem, N_Vector constraints)
-- int KINSetSysFunc(void *kinmem, KINSysFn func)
--
-- # solver routine
-- int KINSol(void *kinmem, N_Vector uu, int strategy, N_Vector u_scale, N_Vector f_scale)
--
-- # optional output routines.
-- # Documentation see kinsol.h line 670-735
-- int KINGetWorkSpace(void *kinmem, long int *lenrw, long int *leniw)
-- int KINGetNumNonlinSolvIters(void *kinmem, long int *nniters)
-- int KINGetNumFuncEvals(void *kinmem, long int *nfevals)
-- int KINGetNumBetaCondFails(void *kinmem, long int *nbcfails)
-- int KINGetNumBacktrackOps(void *kinmem, long int *nbacktr)
-- int KINGetFuncNorm(void *kinmem, realtype *fnorm)
-- int KINGetStepLength(void *kinmem, realtype *steplength)
-- char *KINGetReturnFlagName(int flag)
--
-- # fuction used to deallocate memory used by KINSOL
-- void KINFree(void **kinmem)
--
--# linear solver modules
--cdef extern from "kinsol/kinsol_dense.h":
-- int KINDense(void *kinmem, int N)
--
--cdef extern from "kinpinv.h":
-- int KINPinv(void *kinmem, int N)
--
--cdef extern from "kinslug.h":
-- int KINSLUG(void *kinmem, int N)
--
--# functions used for supplying jacobian, and receiving info from linear solver
--cdef extern from "kinsol/kinsol_direct.h":
-- # user functions
-- ctypedef int (*KINDlsDenseJacFn)(int N, N_Vector u, N_Vector fu, DlsMat J, void *user_data, N_Vector tmp1, N_Vector tmp2)
--
-- # function used to link user functions to KINSOL
-- int KINDlsSetDenseJacFn(void *kinmem, KINDlsDenseJacFn jac)
--
-- # optional output fcts for linear direct solver
-- int KINDlsGetWorkSpace(void *kinmem, long int *lenrwB, long int *leniwB)
-- int KINDlsGetNumJacEvals(void *kinmem, long int *njevalsB)
-- int KINDlsGetNumFuncEvals(void *kinmem, long int *nfevalsB)
-- int KINDlsGetLastFlag(void *kinmem, int *flag)
-- char *KINDlsGetReturnFlagName(int flag)
--
--cdef extern from "kinsol_jmod_wSLU.h":
-- # user functions
-- ctypedef int (*KINPinvJacFn)(int N, N_Vector u, N_Vector fu, DlsMat J, void *user_data, N_Vector tmp1, N_Vector tmp2)
-- ctypedef int (*KINSLUGJacFn)(int N, N_Vector u, N_Vector fu, SuperMatrix *J, void *user_data, N_Vector tmp1, N_Vector tmp2)
--
-- # function used to link user jacobian to KINSOL
-- int KINPinvSetJacFn(void *kinmem, KINPinvJacFn jac)
-- int KINSLUGSetJacFn(void *kinmem, KINSLUGJacFn jac)
--
-- # functions used to set regularizationparameter
-- int KINSLUGSetRegParam(void *kinmem, realtype reg_p)
-- int KINPinvSetRegParam(void *kinmem, realtype reg_p)
--
-- # optional output fcts for linear direct solver
-- int KINPinvGetWorkSpace(void *kinmem, long int *lenrwB, long int *leniwB)
-- int KINPinvGetNumJacEvals(void *kinmem, long int *njevalsB)
-- int KINPinvGetNumFuncEvals(void *kinmem, long int *nfevalsB)
-- int KINPinvGetLastFlag(void *kinmem, int *flag)
-- char *KINPinvGetReturnFlagName(int flag)
--
--
--#=========================
--# END SUNDIALS DEFINITIONS
--#=========================
--
--#===========================
--# Problem information struct
--#===========================
--cdef class ProblemData:
-- cdef:
-- void *RHS # Residual or right-hand-side
-- void *JAC
-- int dim # Dimension of the problem
--
--#=================
--# Module functions
--#=================
--
--cdef inline N_Vector arr2nv(x):
-- x=np.array(x)
-- cdef long int n = len(x)
-- cdef ndarray[double, ndim=1,mode='c'] ndx=x
-- import_array()
-- cdef void* data_ptr=PyArray_DATA(ndx)
-- cdef N_Vector v=N_VNew_Serial(n)
-- memcpy((<N_VectorContent_Serial>v.content).data, data_ptr, n*sizeof(double))
-- return v
--
--cdef inline nv2arr(N_Vector v):
-- cdef long int n = (<N_VectorContent_Serial>v.content).length
-- cdef realtype* v_data = (<N_VectorContent_Serial>v.content).data
-- cdef long int i
-- import_array()
-- cdef ndarray x=np.empty(n)
-- #cdef ndarray x = PyArray_SimpleNewFromData(1, &dims, NPY_DOUBLE,v_data)
-- memcpy(x.data, v_data, n*sizeof(double))
-- return x
--
--cdef inline realtype2arr(realtype *data, int n):
-- """ Create new numpy array from realtype*"""
-- import_array()
-- cdef ndarray[realtype, ndim=1, mode='c'] x=np.empty(n)
-- memcpy(x.data, data, n*sizeof(double))
-- return x
--
--cdef inline arr2realtype(ndarray x, realtype* out_data, int n):
-- """ Create new realtype* from numpy """
-- cdef ndarray[double, ndim=1,mode='c'] ndx=x
-- import_array()
-- cdef void* data_ptr=PyArray_DATA(ndx)
-- memcpy(out_data, data_ptr, n*sizeof(double))
--
--
--cdef inline arr2int(ndarray x, int* out_data, int n):
-- """ Create new int* from numpy array """
-- cdef ndarray[int, ndim=1,mode='c'] ndx=x
-- import_array()
-- cdef void* data_ptr=PyArray_DATA(ndx)
-- memcpy(out_data, data_ptr, n*sizeof(int))
--
-Index: src/lib/sundials_kinsol_includes.pxi
-===================================================================
---- assimulo/lib/sundials_kinsol_includes.pxi (revision 838)
-+++ assimulo/lib/sundials_kinsol_includes.pxi (nonexistent)
-@@ -1,267 +0,0 @@
--#!/usr/bin/env python
--# -*- coding: utf-8 -*-
--
--# Copyright (C) 2010 Modelon AB
--#
--# This program is free software: you can redistribute it and/or modify
--# it under the terms of the GNU Lesser General Public License as published by
--# the Free Software Foundation, version 3 of the License.
--#
--# This program is distributed in the hope that it will be useful,
--# but WITHOUT ANY WARRANTY; without even the implied warranty of
--# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
--# GNU Lesser General Public License for more details.
--#
--# You should have received a copy of the GNU Lesser General Public License
--# along with this program. If not, see <http://www.gnu.org/licenses/>.
--
--"""
--Cython Wrapper for interfacing Python with KINSOL (Sundials Version 2.4.0)
--Johan Ylikiiskilä Modelon AB
--
--see also Jon Olav Vik:
--http://codespeak.net/pipermail/cython-dev/2009-June/005947.html
--
--"""
--
--import numpy as np
--from numpy cimport NPY_DOUBLE, npy_intp, NPY_INT
--
--#==============================================
--#External definitions from numpy headers
--#==============================================
--cdef extern from "numpy/arrayobject.h":
--
-- ctypedef int npy_intp
--
-- ctypedef extern class numpy.dtype [object PyArray_Descr]:
-- cdef int type_num, elsize, alignment
-- cdef char type, kind, byteorder, hasobject
-- cdef object fields, typeobj
--
-- ctypedef int intp
-- ctypedef extern class numpy.ndarray [object PyArrayObject]:
-- cdef char *data
-- cdef int nd
-- cdef intp *dimensions
-- cdef intp *strides
-- cdef int flags
--
-- cdef object PyArray_SimpleNew(int nd, npy_intp* dims, int typenum)
-- cdef object PyArray_SimpleNewFromData(int nd, npy_intp *dims,
-- int typenum, void *data)
-- void import_array()
-- void* PyArray_GetPtr(ndarray aobj, npy_intp* ind)
-- void *PyArray_DATA(ndarray aobj)
--
--import_array()
--
--#==============================================
--# C headers
--#==============================================
--cdef extern from "string.h":
-- void *memcpy(void *s1, void *s2, int n)
--cdef extern from "stdio.h":
-- int printf (char *__format, ...)
--cdef extern from "stdlib.h":
-- ctypedef unsigned long size_t
-- void *calloc(size_t nelem, size_t elsize)
--
--#==============================================
--#External definitions from Sundials headers
--#==============================================
--
--# import data types
--# basic data types
--cdef extern from "sundials/sundials_types.h":
-- ctypedef double realtype
-- ctypedef bint booleantype
--
--# sundials handling of vectors, so far only serial,
--# parallel to be implemented later?
--cdef extern from "sundials/sundials_nvector.h":
-- cdef struct _generic_N_Vector:
-- void* content
-- ctypedef _generic_N_Vector* N_Vector
--
--cdef extern from "nvector/nvector_serial.h":
-- cdef struct _N_VectorContent_Serial:
-- long int length
-- realtype* data
-- ctypedef _N_VectorContent_Serial* N_VectorContent_Serial
-- N_Vector N_VNew_Serial(long int vec_length)
-- void N_VDestroy_Serial(N_Vector v)
-- void N_VPrint_Serial(N_Vector v)
-- cdef N_Vector N_VMake_Serial(long int vec_length, realtype *v_data)
-- N_Vector *N_VCloneVectorArray_Serial(int count, N_Vector w)
-- N_Vector *N_VCloneVectorArrayEmpty_Serial(int count, N_Vector w)
-- void N_VSetArrayPointer_Serial(realtype *v_data, N_Vector v)
-- void N_VConst_Serial(realtype c, N_Vector z)
--
--#Struct for handling the Jacobian data
--cdef extern from "sundials/sundials_direct.h":
-- cdef struct _DlsMat:
-- int type
-- int M
-- int N
-- int ldim
-- int mu
-- int ml
-- int s_mu
-- realtype *data
-- int ldata
-- realtype **cols
-- ctypedef _DlsMat* DlsMat
-- cdef realtype* DENSE_COL(DlsMat A, int j)
--
--# KINSOL functions and routines
--cdef extern from "kinsol/kinsol.h":
-- # user defined functions
-- ctypedef int (*KINSysFn)(N_Vector uu, N_Vector fval, void *user_data )
-- ctypedef void (*KINErrHandlerFn)(int error_code, char *module, char *function, char *msg, void *user_data)
-- ctypedef void (*KINInfoHandlerFn)(char *module, char *function, char *msg, void *user_data)
-- # initialization routines
-- void *KINCreate()
-- int KINInit(void *kinmem, KINSysFn func, N_Vector tmpl)
--
-- # optional input spec. functions,
-- # for specificationsdocumentation cf. kinsol.h line 218-449
-- int KINSetErrHandlerFn(void *kinmem, KINErrHandlerFn ehfun, void *eh_data)
-- int KINSetInfoHandlerFn(void *kinmem, KINInfoHandlerFn ihfun, void *ih_data)
-- int KINSetUserData(void *kinmem, void *user_data)
-- int KINSetPrintLevel(void *kinmemm, int printfl)
-- int KINSetNumMaxIters(void *kinmem, long int mxiter)
-- int KINSetNoInitSetup(void *kinmem, booleantype noInitSetup)
-- int KINSetNoResMon(void *kinmem, booleantype noNNIResMon)
-- int KINSetMaxSetupCalls(void *kinmem, long int msbset)
-- int KINSetMaxSubSetupCalls(void *kinmem, long int msbsetsub)
-- int KINSetEtaForm(void *kinmem, int etachoice)
-- int KINSetEtaConstValue(void *kinmem, realtype eta)
-- int KINSetEtaParams(void *kinmem, realtype egamma, realtype ealpha)
-- int KINSetResMonParams(void *kinmem, realtype omegamin, realtype omegamax)
-- int KINSetResMonConstValue(void *kinmem, realtype omegaconst)
-- int KINSetNoMinEps(void *kinmem, booleantype noMinEps)
-- int KINSetMaxNewtonStep(void *kinmem, realtype mxnewtstep)
-- int KINSetMaxBetaFails(void *kinmem, long int mxnbcf)
-- int KINSetRelErrFunc(void *kinmem, realtype relfunc)
-- int KINSetFuncNormTol(void *kinmem, realtype fnormtol)
-- int KINSetScaledStepTol(void *kinmem, realtype scsteptol)
-- int KINSetConstraints(void *kinmem, N_Vector constraints)
-- int KINSetSysFunc(void *kinmem, KINSysFn func)
--
-- # solver routine
-- int KINSol(void *kinmem, N_Vector uu, int strategy, N_Vector u_scale, N_Vector f_scale)
--
-- # optional output routines.
-- # Documentation see kinsol.h line 670-735
-- int KINGetWorkSpace(void *kinmem, long int *lenrw, long int *leniw)
-- int KINGetNumNonlinSolvIters(void *kinmem, long int *nniters)
-- int KINGetNumFuncEvals(void *kinmem, long int *nfevals)
-- int KINGetNumBetaCondFails(void *kinmem, long int *nbcfails)
-- int KINGetNumBacktrackOps(void *kinmem, long int *nbacktr)
-- int KINGetFuncNorm(void *kinmem, realtype *fnorm)
-- int KINGetStepLength(void *kinmem, realtype *steplength)
-- char *KINGetReturnFlagName(int flag)
--
-- # fuction used to deallocate memory used by KINSOL
-- void KINFree(void **kinmem)
--
--# linear solver modules
--cdef extern from "kinsol/kinsol_dense.h":
-- int KINDense(void *kinmem, int N)
--
--cdef extern from "kinpinv.h":
-- int KINPinv(void *kinmem, int N)
--
--# functions used for supplying jacobian, and receiving info from linear solver
--cdef extern from "kinsol/kinsol_direct.h":
-- # user functions
-- ctypedef int (*KINDlsDenseJacFn)(int N, N_Vector u, N_Vector fu, DlsMat J, void *user_data, N_Vector tmp1, N_Vector tmp2)
--
-- # function used to link user functions to KINSOL
-- int KINDlsSetDenseJacFn(void *kinmem, KINDlsDenseJacFn jac)
--
-- # optional output fcts for linear direct solver
-- int KINDlsGetWorkSpace(void *kinmem, long int *lenrwB, long int *leniwB)
-- int KINDlsGetNumJacEvals(void *kinmem, long int *njevalsB)
-- int KINDlsGetNumFuncEvals(void *kinmem, long int *nfevalsB)
-- int KINDlsGetLastFlag(void *kinmem, int *flag)
-- char *KINDlsGetReturnFlagName(int flag)
--
--cdef extern from "kinsol_jmod.h":
-- # user functions
-- ctypedef int (*KINPinvJacFn)(int N, N_Vector u, N_Vector fu, DlsMat J, void *user_data, N_Vector tmp1, N_Vector tmp2)
--
-- # function used to link user jacobian to KINSOL
-- int KINPinvSetJacFn(void *kinmem, KINPinvJacFn jac)
--
-- # functions used to set regularizationparameter
-- int KINPinvSetRegParam(void *kinmem, realtype reg_p)
--
-- # optional output fcts for linear direct solver
-- int KINPinvGetWorkSpace(void *kinmem, long int *lenrwB, long int *leniwB)
-- int KINPinvGetNumJacEvals(void *kinmem, long int *njevalsB)
-- int KINPinvGetNumFuncEvals(void *kinmem, long int *nfevalsB)
-- int KINPinvGetLastFlag(void *kinmem, int *flag)
-- char *KINPinvGetReturnFlagName(int flag)
--
--
--#=========================
--# END SUNDIALS DEFINITIONS
--#=========================
--
--#===========================
--# Problem information struct
--#===========================
--cdef class ProblemData:
-- cdef:
-- void *RHS # Residual or right-hand-side
-- void *JAC
-- int dim # Dimension of the problem
--
--#=================
--# Module functions
--#=================
--
--cdef inline N_Vector arr2nv(x):
-- x=np.array(x)
-- cdef long int n = len(x)
-- cdef ndarray[double, ndim=1,mode='c'] ndx=x
-- import_array()
-- cdef void* data_ptr=PyArray_DATA(ndx)
-- cdef N_Vector v=N_VNew_Serial(n)
-- memcpy((<N_VectorContent_Serial>v.content).data, data_ptr, n*sizeof(double))
-- return v
--
--cdef inline nv2arr(N_Vector v):
-- cdef long int n = (<N_VectorContent_Serial>v.content).length
-- cdef realtype* v_data = (<N_VectorContent_Serial>v.content).data
-- cdef long int i
-- import_array()
-- cdef ndarray x=np.empty(n)
-- #cdef ndarray x = PyArray_SimpleNewFromData(1, &dims, NPY_DOUBLE,v_data)
-- memcpy(x.data, v_data, n*sizeof(double))
-- return x
--
--cdef inline realtype2arr(realtype *data, int n):
-- """ Create new numpy array from realtype*"""
-- import_array()
-- cdef ndarray[realtype, ndim=1, mode='c'] x=np.empty(n)
-- memcpy(x.data, data, n*sizeof(double))
-- return x
--
--cdef inline arr2realtype(ndarray x, realtype* out_data, int n):
-- """ Create new realtype* from numpy """
-- cdef ndarray[double, ndim=1,mode='c'] ndx=x
-- import_array()
-- cdef void* data_ptr=PyArray_DATA(ndx)
-- memcpy(out_data, data_ptr, n*sizeof(double))
--
--
--cdef inline arr2int(ndarray x, int* out_data, int n):
-- """ Create new int* from numpy array """
-- cdef ndarray[int, ndim=1,mode='c'] ndx=x
-- import_array()
-- cdef void* data_ptr=PyArray_DATA(ndx)
-- memcpy(out_data, data_ptr, n*sizeof(int))
--
-Index: src/lib/reg_routines.c
-===================================================================
---- assimulo/lib/reg_routines.c (revision 838)
-+++ assimulo/lib/reg_routines.c (nonexistent)
-@@ -1,202 +0,0 @@
--/*
-- * Copyright (C) 2010 Modelon AB
-- *
-- * This program is free software: you can redistribute it and/or modify
-- * it under the terms of the GNU Lesser General Public License as published by
-- * the Free Software Foundation, version 3 of the License.
--
-- * This program is distributed in the hope that it will be useful,
-- * but WITHOUT ANY WARRANTY; without even the implied warranty of
-- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-- * GNU Lesser General Public License for more details.
--
-- * You should have received a copy of the GNU Lesser General Public License
-- * along with this program. If not, see <http://www.gnu.org/licenses/>.
-- *
-- * Johan Ylikiiskilä - johan.ylikiiskila@gmail.com
-- *
-- */
--
--#include "reg_routines.h"
--#include <math.h>
--
--SuperMatrix* regSparseMatrix(SuperMatrix *jac, double h)
--{
-- SuperMatrix *res = NULL;
-- int i=0,j=0,k=0,eli=0,elj=0,rowi=0,rowj=0,nnz=0,jnnz=0,jn=0,is_zero=0,max_nz=0;
-- NCformat *Jstore = NULL;
-- double *nzt = NULL, *jnzv=NULL,*nzval=NULL;
-- double reg_param = h*h;
-- int *rit = NULL, *colptr=NULL, *jri=NULL, *jcp=NULL,*rowind=NULL;
-- Stype_t S = SLU_NC;
-- Dtype_t D = SLU_D;
-- Mtype_t M = SLU_GE;
--
-- double *tmp_nzv = NULL;
-- int *tmp_ri = NULL;
--
--
-- /* Extract data */
-- Jstore = jac->Store;
-- jn = jac->nrow;
-- jnnz = Jstore->nnz;
-- jnzv = Jstore->nzval;
-- jri = Jstore->rowind;
-- jcp = Jstore->colptr;
--
-- /* Initialize data and allocate memory for result */
-- max_nz = 2*jnnz;
-- res = calloc(1,sizeof(SuperMatrix));
-- nnz = 0;
-- nzt = calloc(max_nz,sizeof(double));
-- rit = calloc(max_nz,sizeof(int));
-- colptr = calloc(jn+1,sizeof(int));
--
-- /* Calculate nnz,nzval,rowind and colptr */
-- colptr[0] = 0;
--
-- /* Iterate over the columns of both J^T and J */
-- for (i = 0;i < jn;i++) {
-- for (j = 0;j < jn;j++) {
-- /* Get first index of first elt in both columns */
-- rowi = jcp[i];
-- rowj = jcp[j];
--
-- if (i==j) {
-- /* Diagonal elt, perform regularization */
-- is_zero = 0;
-- rit[nnz] = j;
-- nzt[nnz++] += reg_param;
-- } else {
-- is_zero = 1;
-- }
--
-- /* Chack for matching and perform multiplication */
-- while ((rowi < jcp[i+1])&&(rowj < jcp[j+1])) {
-- /* Get row numbers of the two current elts */
-- eli = jri[rowi];
-- elj = jri[rowj];
-- if (eli == elj) {
-- /* Match! Non-zero elt to be inserted! */
-- /* If it is the first time, add to the non_zero structure */
-- if (is_zero) {
-- is_zero = 0;
-- rit[nnz++] = j;
-- }
-- nzt[nnz-1] += jnzv[rowi]*jnzv[rowj];
-- rowi++;
-- rowj++;
-- } else if (eli < elj) {
-- rowi++;
-- } else {
-- rowj++;
-- }
-- }
-- }
-- /* New column, add info to colptr */
-- colptr[i+1] = nnz;
--
-- /*
-- * Check if we need to expand the allocated memory
-- * and expand if necessary
-- */
--
-- if (nnz > (max_nz-jn)) {
-- tmp_nzv = calloc(2*max_nz,sizeof(double));
-- tmp_ri = calloc(2*max_nz,sizeof(int));
--
-- for (k = 0; k < nnz; k++) {
-- tmp_nzv[k] = nzt[k];
-- tmp_ri[k] = rit[k];
-- }
--
-- free(nzt);
-- free(rit);
--
-- nzt = tmp_nzv;
-- rit = tmp_ri;
--
-- tmp_nzv = NULL;
-- tmp_ri = NULL;
--
-- max_nz = 2*max_nz;
-- }
-- }
--
-- /* Reallocate to the needed size of storage */
-- nzval = realloc(nzt,nnz*sizeof(double));
-- rowind = realloc(rit,nnz*sizeof(int));
--
-- /* Now create the matrix */
-- dCreate_CompCol_Matrix(res, jn, jn, nnz, nzval, rowind, colptr, S, D, M);
-- return res ;
--}
--
--SuperMatrix* getRegRHS( SuperMatrix *jac, SuperMatrix *B) {
-- SuperMatrix *res;
-- NCformat *Jstore;
-- DNformat *Bstore;
-- double *data, *jac_data, *rhs_data;
-- int *jac_ri, *jac_cp;
-- int n,i,j,k ;
--
-- /* Extract data from Jacobian */
-- Jstore = jac->Store;
-- jac_data = Jstore->nzval;
-- jac_ri = Jstore->rowind;
-- jac_cp = Jstore->colptr;
--
-- /* Extract data from right hand side */
-- n = B->nrow;
-- Bstore = B->Store;
-- rhs_data = Bstore->nzval;
--
-- /* Allocate data for result */
-- data = calloc(n,sizeof(double));
--
-- /* Calculate data for result */
-- for (i = 0; i< n; i++) {
-- for (j = jac_cp[i]; j < jac_cp[i+1]; j++) {
-- k = jac_ri[j] ;
-- data[i] += jac_data[j]*rhs_data[k];
-- }
-- }
--
-- /* Create the Matrix in SuperMatrix format */
-- res = calloc(1,sizeof(SuperMatrix));
-- dCreate_Dense_Matrix(res,n,1,data,n,SLU_DN,SLU_D,SLU_GE);
--
-- return res;
--}
--
--double getRegParam(SuperMatrix *jac, SuperMatrix *B) {
-- double res = 0;
-- NCformat *Jstore;
-- DNformat *Bstore;
-- double data, *jac_data, *rhs_data;
-- int *jac_ri, *jac_cp;
-- int n,i,j,k ;
--
-- /* Extract data from Jacobian */
-- Jstore = jac->Store;
-- jac_data = Jstore->nzval;
-- jac_ri = Jstore->rowind;
-- jac_cp = Jstore->colptr;
--
-- /* Extract data from right hand side */
-- n = B->nrow;
-- Bstore = B->Store;
-- rhs_data = Bstore->nzval;
--
-- /* Calculate data for result */
-- for (i = 0; i< n; i++) {
-- data = 0;
-- for (j = jac_cp[i]; j < jac_cp[i+1]; j++) {
-- k = jac_ri[j] ;
-- data += jac_data[j]*rhs_data[k];
-- }
-- res += data*data;
-- }
--
-- return sqrt(res);
--}
-Index: src/lib/sundials_kinsol_core.pyx
-===================================================================
---- assimulo/lib/sundials_kinsol_core.pyx (revision 838)
-+++ assimulo/lib/sundials_kinsol_core.pyx (nonexistent)
-@@ -1,371 +0,0 @@
--#!/usr/bin/env python
--# -*- coding: utf-8 -*-
--
--# Copyright (C) 2010 Modelon AB
--#
--# This program is free software: you can redistribute it and/or modify
--# it under the terms of the GNU Lesser General Public License as published by
--# the Free Software Foundation, version 3 of the License.
--#
--# This program is distributed in the hope that it will be useful,
--# but WITHOUT ANY WARRANTY; without even the implied warranty of
--# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
--# GNU Lesser General Public License for more details.
--#
--# You should have received a copy of the GNU Lesser General Public License
--# along with this program. If not, see <http://www.gnu.org/licenses/>.
--
--"""
--Cython Wrapper for interfacing Python with KINSOL (Sundials Version 2.4.0)
--Johan Ylikiiskilä Modelon AB
--
--see also Jon Olav Vik:
--http://codespeak.net/pipermail/cython-dev/2009-June/005947.html
--
--"""
--
--from __future__ import division
--
--include "sundials_kinsol_includes.pxi" # Includes fcts from other header files
--include "sundials_kinsol_core.pxi" # Includes the constants (textual include)
--
--cdef int kin_res(N_Vector xv, N_Vector fval, void *problem_data):
-- """
-- Residual fct called by KINSOL
-- """
--
-- cdef:
-- int i
-- ProblemData pData = <ProblemData>problem_data
-- ndarray[realtype, ndim = 1, mode = 'c'] rhs # Container holding return from user fct
--
-- ndarray x = nv2arr(xv)
-- realtype* resptr = (<N_VectorContent_Serial>fval.content).data
--
-- try:
-- rhs = (<object>pData.RHS)(x)
-- for i in range(pData.dim):
--
-- resptr[i] = rhs[i]
--
-- return KIN_SUCCESS
-- except:
-- return KIN_REC_ERR
--
--cdef int kin_jac(int Neq, N_Vector xv, N_Vector fval, DlsMat Jacobian,
-- void *problem_data, N_Vector tmp1, N_Vector tmp2):
-- """
-- This method is used to connect the Assimulo.Problem.jac to the Sundials
-- Jacobian function.
-- """
-- cdef:
-- ProblemData pData = <ProblemData>problem_data
-- realtype* col_i=DENSE_COL(Jacobian,0)
-- ndarray x = nv2arr(xv)
--
-- try:
--
-- jac=(<object>pData.JAC)(x)
-- #This needs further investigations:
-- #memcpy(Jacobian.data,<realtype*>jac.data, pData.memSizeJac)
--
-- for i in range(Neq):
-- col_i = DENSE_COL(Jacobian, i)
-- for j in range(Neq):
-- col_i[j] = jac[j,i]
--
-- return KINDLS_SUCCESS
-- except:
-- return KINDLS_JACFUNC_RECVR #Recoverable Error (See Sundials description)
--
--
--class KINError(Exception):
-- """
-- Kinsol exception
-- """
--
-- msg = {KIN_MEM_FAIL : 'Memory allocation failed.',
-- KIN_MEM_NULL : 'KINSOL not properly created.',
-- KIN_ILL_INPUT: 'There was an input error.',
-- KIN_NO_MALLOC: 'Memory not allocated, call KINSOL_init(...)',
-- KIN_LINESEARCH_NONCONV: 'Linesearch could not find a step big enough.',
-- KIN_MAXITER_REACHED: 'Max number of iterations reached',
-- KIN_MXNEWT_5X_EXCEEDED: 'Max size of newton step exceeded 5 times.',
-- KIN_LINESEARCH_BCFAIL: 'Linesearch beta-test failed, probably because of too poor progress.',
-- KIN_LINSOLV_NO_RECOVERY: 'psolve encountered an error, but preconditioner is current',
-- KIN_LINIT_FAIL: 'Init of linear solver failed.',
-- KIN_LSETUP_FAIL: 'pset (preconditioner setup fct) encountered an error',
-- KIN_LSOLVE_FAIL: 'Error in either psolve or in linear solver routine.',
-- KIN_SYSFUNC_FAIL: 'Call to RHS failed.',
-- KIN_FIRST_SYSFUNC_ERR: 'Call to RHS failed on first call',
-- KIN_REPTD_SYSFUNC_ERR: 'Call to RHS failed multiple times.',
-- KIN_STEP_LT_STPTOL: 'Scaled step length too small. Either an approximate solution or a local minimum is reached. Check value of residual.',
-- 234: 'KINSOL not compiled with SuperLU, sparse functionality not available.'}
-- value = 0
-- def __init__(self, value):
-- self.value = value
--
-- def __str__(self):
-- try:
-- return repr(self.msg[self.value])
-- except KeyError:
-- return repr('KINSOL failed with flag %s.'%(self.value))
--
--cdef class KINSOL_wrap:
--
-- cdef:
-- void* solver
-- ProblemData pData # A struct containing problem data
-- #ProblemData *ppData # Pointer to above mentioned struct
-- N_Vector x_cur,x_init, x_scale, f_scale, con_nv
-- booleantype noInitSetup, sparse
-- realtype norm_of_res,reg_param
-- ndarray con
-- int print_level
-- ndarray fscale
--
-- def __cinit__(self):
-- self.pData = ProblemData() #Create a new problem struct
-- #self.ppData = &self.pData
--
-- cpdef KINSOL_set_problem_info(self, RHS, dim, JAC= None):
-- """
-- Sets the problem info to the desired struct
-- """
--
-- # Sets residual function
-- self.pData.RHS = <void*>RHS
-- self.pData.dim = dim
-- if JAC != None:
-- self.pData.JAC = <void*>JAC
-- else:
-- self.pData.JAC = NULL
--
-- def KINSOL_init(self,RHS,x0,dim, JAC = None, con = None,sparse = False, print_level = 0,norm_of_res = 0,reg_param=0,fscale=None):
-- """
-- Initializes solver
-- """
-- cdef int flag
-- # Set problem info
-- self.noInitSetup = False
-- self.print_level = print_level
-- self.norm_of_res = norm_of_res
-- self.reg_param = reg_param
-- self.KINSOL_set_problem_info(RHS,dim,JAC)
-- self.con = con
-- self.sparse = sparse
-- self.fscale = fscale
--
--
-- # Create initial guess from the supplied numpy array
-- # print "x0 got in KINSOL wrapper: ", x0
-- self.x_cur = arr2nv(x0)
-- self.x_init = arr2nv(x0)
--
--
-- if self.solver == NULL: # solver runs for the first time
--
-- # Create solver
-- self.solver = KINCreate()
-- if self.solver == NULL:
-- raise KINError(KIN_MEM_FAIL)
-- if self.print_level >0:
-- print "KINSOL created"
--
-- # Stop solver from performing precond setup
-- flag = KINSetNoInitSetup(self.solver, self.noInitSetup)
-- if flag < 0:
-- raise KINError(flag)
--
-- flag = KINSetPrintLevel(self.solver, self.print_level)
-- if flag < 0:
-- raise KINError(flag)
--
-- if self.norm_of_res >0:
-- flag = KINSetMaxNewtonStep(self.solver, self.norm_of_res)
-- if flag < 0:
-- raise KINError(flag)
-- else:
-- flag = KINSetMaxNewtonStep(self.solver, 1e20)
-- if flag < 0:
-- raise KINError(flag)
--
-- # If the user has specified constraints, connect them
-- if self.con is not None:
-- if self.print_level >0:
-- print "Applying constraints"
-- self.con_nv = arr2nv(self.con)
-- flag = KINSetConstraints(self.solver, self.con_nv)
-- if flag < 0:
-- raise KINError(flag)
--
-- # Allocate internal memory
-- flag = KINInit(self.solver,kin_res,self.x_cur)
-- if flag < 0:
-- raise KINError(flag)
-- if self.print_level >0:
-- print "KINSOL initialized"
--
-- # Link to linear solver
-- if self.sparse:
-- raise KINError(234)
-- else:
-- flag = KINPinv(self.solver,self.pData.dim)
-- #flag = KINDense(self.solver,self.pData.dim)
--
-- if flag < 0:
-- raise KINError(flag)
-- if self.print_level >0:
-- print "Linear solver connected"
--
-- # Set regularization parameter, if necessary
-- if self.reg_param != 0.0:
-- if self.sparse:
-- raise KINError(234)
-- else:
-- flag = KINPinvSetRegParam(self.solver, self.reg_param)
--
-- if flag < 0:
-- raise KINError(flag)
-- if self.print_level >0:
-- print "Reg param ", self.reg_param, " set"
-- else:
-- # If the user has specified constraints, connect them
-- if con is not None:
-- if self.print_level >0:
-- print "Applying constraints"
-- self.con_nv = arr2nv(con)
-- flag = KINSetConstraints(self.solver, self.con_nv)
-- if flag < 0:
-- raise KINError(flag)
-- else:
-- flag = KINSetConstraints(self.solver, NULL)
-- if flag < 0:
-- raise KINError(flag)
--
-- # If the user supplied a Jacobien, link it to the solver
-- if self.pData.JAC != NULL:
-- if sparse:
-- raise KINError(234)
-- else:
-- flag = KINPinvSetJacFn(self.solver,kin_jac)
-- #flag = KINDlsSetDenseJacFn(self.solver,kin_jac)
--
-- if flag < 0:
-- raise KINError(flag)
-- if self.print_level >0:
-- print "Jacobian supplied by user connected"
-- else:
-- if sparse:
-- raise KINError(234)
-- else:
-- flag = KINPinvSetJacFn(self.solver,NULL)
-- #flag = KINDlsSetDenseJacFn(self.solver,NULL)
--
-- if flag < 0:
-- raise KINError(flag)
--
-- # Link the solver to the supplied problem data
-- flag = KINSetUserData(self.solver,<void*>self.pData)
-- if flag < 0:
-- raise KINError(flag)
-- if self.print_level >0:
-- print "User data set"
--
-- # Create scaling vectors filled with ones
-- # since the problem is assumed to be scaled
-- self.x_scale = arr2nv(np.ones(self.pData.dim))
-- if self.fscale is not None:
-- self.f_scale = arr2nv(self.fscale)
-- else:
-- self.f_scale = arr2nv(np.ones(self.pData.dim))
--
--
-- def KINSOL_solve(self,wo_LineSearch):
-- """
-- Function that should be called after a call to KINSOL_init
-- solves the function supplied as RHS
-- """
--
-- cdef:
-- int flag, DLSflag, lsflag, count,ind
-- long int nb_iters
-- ndarray x0
-- if self.print_level >0:
-- print "Calling solver..."
--
-- if not wo_LineSearch:
-- # Use LineSearch
-- flag = KINSol(<void*>self.solver,self.x_cur,KIN_LINESEARCH,self.x_scale,self.f_scale)
-- count = 0
-- if flag > 1:
-- # Probably stuck at a minimum
-- self.x_cur = self.x_init
-- flag = KINSol(<void*>self.solver,self.x_cur,KIN_NONE,self.x_scale,self.f_scale)
--
-- if flag > 1:
-- # Stuck at minimum again
-- raise KINError(flag)
-- else:
-- # Don't use linesearch
-- flag = -7
--
-- while flag <0:
-- # Get nbr of non-linear iterations
-- lsflag = KINGetNumNonlinSolvIters(<void*>self.solver, &nb_iters)
-- if lsflag != 0:
-- raise KINError(lsflag)
--
--
-- if (flag == -5 and nb_iters == 1):
-- # Cannot take a step bigger than zero on the first iteration
-- # Try with heuristic initial guess
-- raise KINError(42)
--
-- if (flag == -7 or flag == -6 or (flag == -5 and nb_iters > 1)):
-- if count == 0:
-- # First time
-- print "Calling solver again as simple newton."
-- self.x_cur = self.x_init
-- DLSflag = KINSetMaxSetupCalls(self.solver, 1);
-- if DLSflag < 0:
-- raise KINError(DLSflag)
--
-- elif count == 1:
-- # second time
-- print "Turning of residual monitoring"
-- DLSflag = KINSetNoResMon(self.solver, True);
-- if DLSflag < 0:
-- raise KINError(DLSflag)
--
-- elif count == 3:
-- # fourth time, release the bound on the step
--
-- print "Remove limit on stepsize"
-- DLSflag = KINSetMaxNewtonStep(self.solver, 1.0e34)
-- if DLSflag < 0:
-- raise KINError(DLSflag)
--
-- count += 1
-- if count >= 10:
-- if self.print_level >0:
-- print "Tried with simple newton ten times"
-- raise KINError(-7)
--
-- else:
--
-- print "Error in solve, flag: ", flag
-- lsflag = KINPinvGetLastFlag(self.solver, &lsflag)
-- #lsflag = KINDlsGetLastFlag(self.solver, &lsflag)
-- if lsflag != 0:
-- if lsflag <0:
-- print "Last flag from Linear solver: ", lsflag
-- else:
-- print "Jacobian singular at column ", lsflag
-- raise KINError(flag)
--
-- flag = KINSol(<void*>self.solver,self.x_cur,KIN_NONE,self.x_scale,self.f_scale)
-- if self.print_level >0:
-- print "Problem solved, returning result"
--
-- return nv2arr(self.x_cur)
-Index: src/lib/kinsol_jmod.c
-===================================================================
---- assimulo/lib/kinsol_jmod.c (revision 838)
-+++ assimulo/lib/kinsol_jmod.c (nonexistent)
-@@ -1,481 +0,0 @@
--/*
-- * This file has been modified by Modelon AB, changes are Copyright
-- * (c) 2014 Modelon AB.
-- *
-- * Original copyright notice:
-- *
-- * Copyright (c) 2002, The Regents of the University of California.
-- * Produced at the Lawrence Livermore National Laboratory.
-- * Written by S.D. Cohen, A.C. Hindmarsh, R. Serban,
-- * D. Shumaker, and A.G. Taylor.
-- * UCRL-CODE-155951 (CVODE)
-- * UCRL-CODE-155950 (CVODES)
-- * UCRL-CODE-155952 (IDA)
-- * UCRL-CODE-237203 (IDAS)
-- * UCRL-CODE-155953 (KINSOL)
-- * All rights reserved.
--
-- * This file is part of SUNDIALS.
--
-- * Redistribution and use in source and binary forms, with or without
-- * modification, are permitted provided that the following conditions
-- * are met:
-- *
-- * 1. Redistributions of source code must retain the above copyright
-- * notice, this list of conditions and the disclaimer below.
-- *
-- * 2. Redistributions in binary form must reproduce the above copyright
-- * notice, this list of conditions and the disclaimer (as noted below)
-- * in the documentation and/or other materials provided with the
-- * distribution.
-- *
-- * 3. Neither the name of the UC/LLNL nor the names of its contributors
-- * may be used to endorse or promote products derived from this software
-- * without specific prior written permission.
-- *
-- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-- * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-- * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
-- * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
-- * REGENTS OF THE UNIVERSITY OF CALIFORNIA, THE U.S. DEPARTMENT OF ENERGY
-- * OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-- * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
-- * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
-- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
-- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-- *
-- * Additional BSD Notice
-- * ---------------------
-- * 1. This notice is required to be provided under our contract with
-- * the U.S. Department of Energy (DOE). This work was produced at the
-- * University of California, Lawrence Livermore National Laboratory
-- * under Contract No. W-7405-ENG-48 with the DOE.
-- *
-- * 2. Neither the United States Government nor the University of
-- * California nor any of their employees, makes any warranty, express
-- * or implied, or assumes any liability or responsibility for the
-- * accuracy, completeness, or usefulness of any information, apparatus,
-- * product, or process disclosed, or represents that its use would not
-- * infringe privately-owned rights.
-- *
-- * 3. Also, reference herein to any specific commercial products,
-- * process, or services by trade name, trademark, manufacturer or
-- * otherwise does not necessarily constitute or imply its endorsement,
-- * recommendation, or favoring by the United States Government or the
-- * University of California. The views and opinions of authors expressed
-- * herein do not necessarily state or reflect those of the United States
-- * Government or the University of California, and shall not be used for
-- * advertising or product endorsement purposes.
-- */
--
--/*
-- * =================================================================
-- * IMPORTED HEADER FILES
-- * =================================================================
-- */
--
--#include <stdio.h>
--#include <stdlib.h>
--
--#include "kinsol/kinsol_impl.h"
--#include "kinsol_jmod_impl.h"
--#include <sundials/sundials_math.h>
--
--/*
-- * =================================================================
-- * FUNCTION SPECIFIC CONSTANTS
-- * =================================================================
-- */
--
--/* Constant for DQ Jacobian approximation */
--#define MIN_INC_MULT RCONST(1000.0)
--
--#define ZERO RCONST(0.0)
--#define ONE RCONST(1.0)
--#define TWO RCONST(2.0)
--
--/*
-- * =================================================================
-- * READIBILITY REPLACEMENTS
-- * =================================================================
-- */
--
--#define lrw1 (kin_mem->kin_lrw1)
--#define liw1 (kin_mem->kin_liw1)
--#define uround (kin_mem->kin_uround)
--#define func (kin_mem->kin_func)
--#define user_data (kin_mem->kin_user_data)
--#define printfl (kin_mem->kin_printfl)
--#define linit (kin_mem->kin_linit)
--#define lsetup (kin_mem->kin_lsetup)
--#define lsolve (kin_mem->kin_lsolve)
--#define lfree (kin_mem->kin_lfree)
--#define lmem (kin_mem->kin_lmem)
--#define inexact_ls (kin_mem->kin_inexact_ls)
--#define uu (kin_mem->kin_uu)
--#define fval (kin_mem->kin_fval)
--#define uscale (kin_mem->kin_uscale)
--#define fscale (kin_mem->kin_fscale)
--#define sqrt_relfunc (kin_mem->kin_sqrt_relfunc)
--#define sJpnorm (kin_mem->kin_sJpnorm)
--#define sfdotJp (kin_mem->kin_sfdotJp)
--#define errfp (kin_mem->kin_errfp)
--#define infofp (kin_mem->kin_infofp)
--#define setupNonNull (kin_mem->kin_setupNonNull)
--#define vtemp1 (kin_mem->kin_vtemp1)
--#define vec_tmpl (kin_mem->kin_vtemp1)
--#define vtemp2 (kin_mem->kin_vtemp2)
--
--#define mtype (kinpinv_mem->d_type)
--#define n (kinpinv_mem->d_n)
--#define ml (kinpinv_mem->d_ml)
--#define mu (kinpinv_mem->d_mu)
--#define smu (kinpinv_mem->d_smu)
--#define jacDQ (kinpinv_mem->d_jacDQ)
--#define djac (kinpinv_mem->d_djac)
--#define bjac (kinpinv_mem->d_bjac)
--#define J (kinpinv_mem->d_J)
--#define pivots (kinpinv_mem->d_pivots)
--#define nje (kinpinv_mem->d_nje)
--#define nfeDQ (kinpinv_mem->d_nfeDQ)
--#define J_data (kinpinv_mem->d_J_data)
--#define last_flag (kinpinv_mem->d_last_flag)
--#define RTR (kinpinv_mem->d_RTR)
--#define reg_param (kinpinv_mem->d_reg_param)
--
--/*
-- * =================================================================
-- * EXPORTED FUNCTIONS
-- * =================================================================
-- */
--
--/*
-- * -----------------------------------------------------------------
-- * KINPinvSetJacFn
-- * -----------------------------------------------------------------
-- */
--
--int KINPinvSetJacFn(void *kinmem, KINPinvJacFn jac)
--{
-- KINMem kin_mem;
-- KINPinvMem kinpinv_mem;
--
-- /* Return immediately if kinmem is NULL */
-- if (kinmem == NULL) {
-- KINProcessError(NULL, KINPINV_MEM_NULL, "KINPINV", "KINPinvSetJacFn", MSGD_KINMEM_NULL);
-- return(KINPINV_MEM_NULL);
-- }
-- kin_mem = (KINMem) kinmem;
--
-- if (lmem == NULL) {
-- KINProcessError(kin_mem, KINPINV_LMEM_NULL, "KINPINV", "KINPinvSetJacFn", MSGD_LMEM_NULL);
-- return(KINPINV_LMEM_NULL);
-- }
-- kinpinv_mem = (KINPinvMem) lmem;
--
-- if (jac != NULL) {
-- jacDQ = FALSE;
-- djac = jac;
-- } else {
-- jacDQ = TRUE;
-- }
--
-- return(KINPINV_SUCCESS);
--}
--
--/*
-- * -----------------------------------------------------------------
-- * KINPinvSetRegParam
-- * -----------------------------------------------------------------
-- */
--
--int KINPinvSetRegParam(void *kinmem, realtype reg_p)
--{
-- KINMem kin_mem;
-- KINPinvMem kinpinv_mem;
--
-- /* Return immediately if kinmem is NULL */
-- if (kinmem == NULL) {
-- KINProcessError(NULL, KINPINV_MEM_NULL, "KINPINV", "KINPinvSetJacFn", MSGD_KINMEM_NULL);
-- return(KINPINV_MEM_NULL);
-- }
-- kin_mem = (KINMem) kinmem;
--
-- if (lmem == NULL) {
-- KINProcessError(kin_mem, KINPINV_LMEM_NULL, "KINPINV", "KINPinvSetJacFn", MSGD_LMEM_NULL);
-- return(KINPINV_LMEM_NULL);
-- }
-- kinpinv_mem = (KINPinvMem) lmem;
--
-- reg_param = reg_p;
--
-- return(KINPINV_SUCCESS);
--}
--
--
--/*
-- * -----------------------------------------------------------------
-- * KINPinvGetWorkSpace
-- * -----------------------------------------------------------------
-- */
--
--int KINPinvGetWorkSpace(void *kinmem, long int *lenrwLS, long int *leniwLS)
--{
-- KINMem kin_mem;
-- KINPinvMem kinpinv_mem;
--
-- /* Return immediately if kinmem is NULL */
-- if (kinmem == NULL) {
-- KINProcessError(NULL, KINPINV_MEM_NULL, "KINPINV", "KINPinvGetWorkSpace", MSGD_KINMEM_NULL);
-- return(KINPINV_MEM_NULL);
-- }
-- kin_mem = (KINMem) kinmem;
--
-- if (lmem == NULL) {
-- KINProcessError(kin_mem, KINPINV_LMEM_NULL, "KINPINV", "KINPinvGetWorkSpace", MSGD_LMEM_NULL);
-- return(KINPINV_LMEM_NULL);
-- }
-- kinpinv_mem = (KINPinvMem) lmem;
--
-- if (mtype == SUNDIALS_DENSE) {
-- *lenrwLS = n*n;
-- *leniwLS = n;
-- } else if (mtype == SUNDIALS_BAND) {
-- *lenrwLS = n*(smu + mu + 2*ml + 2);
-- *leniwLS = n;
-- }
--
-- return(KINPINV_SUCCESS);
--}
--
--/*
-- * -----------------------------------------------------------------
-- * KINPinvGetNumJacEvals
-- * -----------------------------------------------------------------
-- */
--
--int KINPinvGetNumJacEvals(void *kinmem, long int *njevals)
--{
-- KINMem kin_mem;
-- KINPinvMem kinpinv_mem;
--
-- /* Return immediately if kinmem is NULL */
-- if (kinmem == NULL) {
-- KINProcessError(NULL, KINPINV_MEM_NULL, "KINPINV", "KINPinvGetNumJacEvals", MSGD_KINMEM_NULL);
-- return(KINPINV_MEM_NULL);
-- }
-- kin_mem = (KINMem) kinmem;
--
-- if (lmem == NULL) {
-- KINProcessError(kin_mem, KINPINV_LMEM_NULL, "KINPINV", "KINPinvGetNumJacEvals", MSGD_LMEM_NULL);
-- return(KINPINV_LMEM_NULL);
-- }
-- kinpinv_mem = (KINPinvMem) lmem;
--
-- *njevals = nje;
--
-- return(KINPINV_SUCCESS);
--}
--
--/*
-- * -----------------------------------------------------------------
-- * KINPinvGetNumFuncEvals
-- * -----------------------------------------------------------------
-- */
--
--int KINPinvGetNumFuncEvals(void *kinmem, long int *nfevalsLS)
--{
-- KINMem kin_mem;
-- KINPinvMem kinpinv_mem;
--
-- /* Return immediately if kinmem is NULL */
-- if (kinmem == NULL) {
-- KINProcessError(NULL, KINPINV_MEM_NULL, "KINPINV", "KINPinvGetNumFuncEvals", MSGD_KINMEM_NULL);
-- return(KINPINV_MEM_NULL);
-- }
-- kin_mem = (KINMem) kinmem;
--
-- if (lmem == NULL) {
-- KINProcessError(kin_mem, KINPINV_LMEM_NULL, "KINPINV", "KINPinvGetNumGuncEvals", MSGD_LMEM_NULL);
-- return(KINPINV_LMEM_NULL);
-- }
-- kinpinv_mem = (KINPinvMem) lmem;
--
-- *nfevalsLS = nfeDQ;
--
-- return(KINPINV_SUCCESS);
--}
--
--/*
-- * -----------------------------------------------------------------
-- * KINPinvGetLastFlag
-- * -----------------------------------------------------------------
-- */
--
--int KINPinvGetLastFlag(void *kinmem, int *flag)
--{
-- KINMem kin_mem;
-- KINPinvMem kinpinv_mem;
--
-- /* Return immediately if kinmem is NULL */
-- if (kinmem == NULL) {
-- KINProcessError(NULL, KINPINV_MEM_NULL, "KINPINV", "KINPinvGetLastFlag", MSGD_KINMEM_NULL);
-- return(KINPINV_MEM_NULL);
-- }
-- kin_mem = (KINMem) kinmem;
--
-- if (lmem == NULL) {
-- KINProcessError(kin_mem, KINPINV_LMEM_NULL, "KINPINV", "KINPinvGetLastFlag", MSGD_LMEM_NULL);
-- return(KINPINV_LMEM_NULL);
-- }
-- kinpinv_mem = (KINPinvMem) lmem;
--
-- *flag = last_flag;
--
-- return(KINPINV_SUCCESS);
--}
--
--/*
-- * -----------------------------------------------------------------
-- * KINPinvGetReturnFlagName
-- * -----------------------------------------------------------------
-- */
--
--char *KINPinvGetReturnFlagName(int flag)
--{
-- char *name;
--
-- name = (char *)malloc(30*sizeof(char));
--
-- switch(flag) {
-- case KINPINV_SUCCESS:
-- sprintf(name, "KINPINV_SUCCESS");
-- break;
-- case KINPINV_MEM_NULL:
-- sprintf(name, "KINPINV_MEM_NULL");
-- break;
-- case KINPINV_LMEM_NULL:
-- sprintf(name, "KINPINV_LMEM_NULL");
-- break;
-- case KINPINV_ILL_INPUT:
-- sprintf(name, "KINPINV_ILL_INPUT");
-- break;
-- case KINPINV_MEM_FAIL:
-- sprintf(name, "KINPINV_MEM_FAIL");
-- break;
-- default:
-- sprintf(name, "NONE");
-- }
--
-- return(name);
--}
--
--
--/*
-- * =================================================================
-- * DQ JACOBIAN APPROXIMATIONS
-- * =================================================================
-- */
--
--
--
--
--/*
-- * -----------------------------------------------------------------
-- * kinPinvDQJac
-- * -----------------------------------------------------------------
-- * This routine generates a dense difference quotient approximation to
-- * the Jacobian of F(u). It assumes that a dense matrix of type
-- * DlsMat is stored column-wise, and that elements within each column
-- * are contiguous. The address of the jth column of J is obtained via
-- * the macro DENSE_COL and this pointer is associated with an N_Vector
-- * using the N_VGetArrayPointer/N_VSetArrayPointer functions.
-- * Finally, the actual computation of the jth column of the Jacobian is
-- * done with a call to N_VLinearSum.
-- *
-- * The increment used in the finite-difference approximation
-- * J_ij = ( F_i(u+sigma_j * e_j) - F_i(u) ) / sigma_j
-- * is
-- * sigma_j = max{|u_j|, |1/uscale_j|} * sqrt(uround)
-- *
-- * Note: uscale_j = 1/typ(u_j)
-- *
-- * NOTE: Any type of failure of the system function her leads to an
-- * unrecoverable failure of the Jacobian function and thus
-- * of the linear solver setup function, stopping KINSOL.
-- * -----------------------------------------------------------------
-- */
--
--int kinPinvDQJac(int N,
-- N_Vector u, N_Vector fu,
-- DlsMat Jac, void *data,
-- N_Vector tmp1, N_Vector tmp2)
--{
-- realtype inc, inc_inv, ujsaved, ujscale, sign;
-- realtype *tmp2_data, *u_data, *uscale_data;
-- N_Vector ftemp, jthCol;
-- long int j;
-- int retval = 0;
--
-- KINMem kin_mem;
-- KINPinvMem kinpinv_mem;
--
-- /* data points to kin_mem */
-- kin_mem = (KINMem) data;
-- kinpinv_mem = (KINPinvMem) lmem;
--
-- /* Save pointer to the array in tmp2 */
-- tmp2_data = N_VGetArrayPointer(tmp2);
--
-- /* Rename work vectors for readibility */
-- ftemp = tmp1;
-- jthCol = tmp2;
--
-- /* Obtain pointers to the data for u and uscale */
-- u_data = N_VGetArrayPointer(u);
-- uscale_data = N_VGetArrayPointer(uscale);
--
-- /* This is the only for loop for 0..N-1 in KINSOL */
--
-- for (j = 0; j < N; j++) {
--
-- /* Generate the jth col of Jac(u) */
--
-- N_VSetArrayPointer(DENSE_COL(Jac,j), jthCol);
--
-- ujsaved = u_data[j];
-- ujscale = ONE/uscale_data[j];
-- sign = (ujsaved >= ZERO) ? ONE : -ONE;
--#if SUNDIALS_26
-- inc = sqrt_relfunc*SUNMAX(SUNRabs(ujsaved), ujscale)*sign;
--#else
-- inc = sqrt_relfunc*MAX(ABS(ujsaved), ujscale)*sign;
--#endif
-- u_data[j] += inc;
--
-- retval = func(u, ftemp, user_data);
-- nfeDQ++;
-- if(retval > 0) {
-- /* try to recover by stepping in the opposite direction */
-- inc = -inc;
-- u_data[j] = ujsaved + inc;
--
-- retval = func(u, ftemp, user_data);
-- nfeDQ++;
-- }
-- if (retval != 0) break;
--
-- u_data[j] = ujsaved;
--
-- inc_inv = ONE/inc;
-- N_VLinearSum(inc_inv, ftemp, -inc_inv, fu, jthCol);
--
-- }
--
-- /* Restore original array pointer in tmp2 */
-- N_VSetArrayPointer(tmp2_data, tmp2);
--
-- return(retval);
--}
-Index: src/lib/kinsol_jmod_impl_wSLU.h
-===================================================================
---- assimulo/lib/kinsol_jmod_impl_wSLU.h (revision 838)
-+++ assimulo/lib/kinsol_jmod_impl_wSLU.h (nonexistent)
-@@ -1,168 +0,0 @@
--/*
-- * This file has been modified by Modelon AB, changes are Copyright
-- * (c) 2014 Modelon AB.
-- *
-- * Original copyright notice:
-- *
-- * Copyright (c) 2002, The Regents of the University of California.
-- * Produced at the Lawrence Livermore National Laboratory.
-- * Written by S.D. Cohen, A.C. Hindmarsh, R. Serban,
-- * D. Shumaker, and A.G. Taylor.
-- * UCRL-CODE-155951 (CVODE)
-- * UCRL-CODE-155950 (CVODES)
-- * UCRL-CODE-155952 (IDA)
-- * UCRL-CODE-237203 (IDAS)
-- * UCRL-CODE-155953 (KINSOL)
-- * All rights reserved.
--
-- * This file is part of SUNDIALS.
--
-- * Redistribution and use in source and binary forms, with or without
-- * modification, are permitted provided that the following conditions
-- * are met:
-- *
-- * 1. Redistributions of source code must retain the above copyright
-- * notice, this list of conditions and the disclaimer below.
-- *
-- * 2. Redistributions in binary form must reproduce the above copyright
-- * notice, this list of conditions and the disclaimer (as noted below)
-- * in the documentation and/or other materials provided with the
-- * distribution.
-- *
-- * 3. Neither the name of the UC/LLNL nor the names of its contributors
-- * may be used to endorse or promote products derived from this software
-- * without specific prior written permission.
-- *
-- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-- * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-- * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
-- * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
-- * REGENTS OF THE UNIVERSITY OF CALIFORNIA, THE U.S. DEPARTMENT OF ENERGY
-- * OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-- * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
-- * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
-- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
-- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-- *
-- * Additional BSD Notice
-- * ---------------------
-- * 1. This notice is required to be provided under our contract with
-- * the U.S. Department of Energy (DOE). This work was produced at the
-- * University of California, Lawrence Livermore National Laboratory
-- * under Contract No. W-7405-ENG-48 with the DOE.
-- *
-- * 2. Neither the United States Government nor the University of
-- * California nor any of their employees, makes any warranty, express
-- * or implied, or assumes any liability or responsibility for the
-- * accuracy, completeness, or usefulness of any information, apparatus,
-- * product, or process disclosed, or represents that its use would not
-- * infringe privately-owned rights.
-- *
-- * 3. Also, reference herein to any specific commercial products,
-- * process, or services by trade name, trademark, manufacturer or
-- * otherwise does not necessarily constitute or imply its endorsement,
-- * recommendation, or favoring by the United States Government or the
-- * University of California. The views and opinions of authors expressed
-- * herein do not necessarily state or reflect those of the United States
-- * Government or the University of California, and shall not be used for
-- * advertising or product endorsement purposes.
-- */
--
--#ifndef _KINJMOD_IMPL_H
--#define _KINJMOD_IMPL_H
--
--#ifdef __cplusplus /* wrapper to enable C++ usage */
--extern "C" {
--#endif
--
--#include "kinsol_jmod_wSLU.h"
--#include "slu_ddefs.h"
--
--
--/*
-- * -----------------------------------------------------------------
-- * Types: KINPinvMemRec, KINPinvMem
-- * -----------------------------------------------------------------
-- * The type KINPinvMem is pointer to a KINPinvMemRec.
-- * This structure contains KINPinv/KINSLUG solver-specific data.
-- * -----------------------------------------------------------------
-- */
--
--typedef struct KINPinvMemRec {
--
-- int d_type; /* SUNDIALS_DENSE or SUNDIALS_BAND */
--
-- int d_n; /* problem dimension */
--
-- int d_ml; /* lower bandwidth of Jacobian */
-- int d_mu; /* upper bandwidth of Jacobian */
-- int d_smu; /* upper bandwith of M = MIN(N-1,d_mu+d_ml) */
--
-- booleantype d_jacDQ; /* TRUE if using internal DQ Jacobian approx. */
-- KINPinvJacFn d_djac; /* dense Jacobian routine to be called */
-- KINSLUGJacFn d_spjac; /* sparse Jacobian routine to be called */
--
-- void *d_J_data; /* J_data is passed to djac or bjac */
--
-- DlsMat d_J; /* problem Jacobian */
--
-- SuperMatrix *sp_J; /* sparse problem jacobian */
-- SuperMatrix *sp_L; /* L in the sparse LU factorization */
-- SuperMatrix *sp_U; /* U in the sparse LU factorization */
-- SuperMatrix *sp_B; /* sparse right hand side */
-- int *sp_perm_r; /* row permutations from partial pivoting */
-- int *sp_perm_c; /* column permutation vector */
--
-- SuperMatrix *sp_JTJ; /* Matrix needed for regularisation */
--
-- superlu_options_t *sp_options; /* options struct for SuperLU */
-- SuperLUStat_t *sp_stat; /* statistcis struct for SuperLU */
--
-- int *d_pivots; /* pivot array for PM = LU */
-- realtype *d_beta;
-- realtype d_reg_param; /* Regularization parameter */
--
-- long int d_nje; /* no. of calls to jac */
--
-- long int d_nfeDQ; /* no. of calls to F due to DQ Jacobian approx. */
--
-- int d_last_flag; /* last error return flag */
--
-- DlsMat d_JTJ;
-- booleantype d_regularized; /* Boolean set to true if problem is regularized*/
-- booleantype d_redojac;
--
--} *KINPinvMem;
--
--
--/*
-- * -----------------------------------------------------------------
-- * Prototypes of internal functions
-- * -----------------------------------------------------------------
-- */
--
--int kinPinvDQJac(int N,
-- N_Vector u, N_Vector fu,
-- DlsMat Jac, void *data,
-- N_Vector tmp1, N_Vector tmp2);
--
--/*
-- * -----------------------------------------------------------------
-- * Error Messages
-- * -----------------------------------------------------------------
-- */
--
--#define MSGD_KINMEM_NULL "KINSOL memory is NULL."
--#define MSGD_BAD_NVECTOR "A required vector operation is not implemented."
--#define MSGD_MEM_FAIL "A memory request failed."
--#define MSGD_LMEM_NULL "Linear solver memory is NULL."
--#define MSGD_BAD_SIZES "Illegal bandwidth parameter(s). Must have 0 <= ml, mu <= N-1."
--#define MSGD_JACFUNC_FAILED "The Jacobian routine failed in an unrecoverable manner."
--
--#ifdef __cplusplus
--}
--#endif
--
--#endif
-Index: src/lib/reg_routines.h
-===================================================================
---- assimulo/lib/reg_routines.h (revision 838)
-+++ assimulo/lib/reg_routines.h (nonexistent)
-@@ -1,43 +0,0 @@
--/*
-- * Copyright (C) 2010 Modelon AB
-- *
-- * This program is free software: you can redistribute it and/or modify
-- * it under the terms of the GNU Lesser General Public License as published by
-- * the Free Software Foundation, version 3 of the License.
--
-- * This program is distributed in the hope that it will be useful,
-- * but WITHOUT ANY WARRANTY; without even the implied warranty of
-- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-- * GNU Lesser General Public License for more details.
--
-- * You should have received a copy of the GNU Lesser General Public License
-- * along with this program. If not, see <http://www.gnu.org/licenses/>.
-- *
-- * Johan Ylikiiskilä - johan.ylikiiskila@gmail.com
-- *
-- */
--
--#ifdef __cplusplus /* wrapper to enable C++ usage */
--extern "C" {
--#endif
--
--#ifndef _REG_ROUTINES_H
--#define _REG_ROUTINES_H
--
--
--#include "slu_ddefs.h"
--
-- /*
-- * Routines used in regularisation
-- */
--
--SuperMatrix* regSparseMatrix( SuperMatrix *jac, double h);
--
--SuperMatrix* getRegRHS( SuperMatrix *jac, SuperMatrix *B);
--
--double getRegParam(SuperMatrix *jac, SuperMatrix *B);
--#endif
--
--#ifdef __cplusplus
--}
--#endif
-Index: src/lib/kinslug.c
-===================================================================
---- assimulo/lib/kinslug.c (revision 838)
-+++ assimulo/lib/kinslug.c (nonexistent)
-@@ -1,678 +0,0 @@
--/*
-- * Copyright (C) 2010 Modelon AB
-- *
-- * This program is free software: you can redistribute it and/or modify
-- * it under the terms of the GNU Lesser General Public License as published by
-- * the Free Software Foundation, version 3 of the License.
--
-- * This program is distributed in the hope that it will be useful,
-- * but WITHOUT ANY WARRANTY; without even the implied warranty of
-- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-- * GNU Lesser General Public License for more details.
--
-- * You should have received a copy of the GNU Lesser General Public License
-- * along with this program. If not, see <http://www.gnu.org/licenses/>.
-- *
-- */
--
--/*
-- * This is a linear solver using Tikhonov regularization and the sparse
-- * solver SuperLU
-- */
--#include <stdio.h>
--#include <stdlib.h>
--#include <math.h>
--
--#include "slu_ddefs.h"
--#include "supermatrix.h"
--#include "reg_routines.h"
--#include "kinsol_jmod_impl_wSLU.h"
--#include "kinslug.h"
--#include "kinsol/kinsol_impl.h"
--
--#include <nvector/nvector_serial.h>
--#include <sundials/sundials_direct.h>
--#include <sundials/sundials_math.h>
--
--/* Constants */
--
--#define ZERO RCONST(0.0)
--#define ONE RCONST(1.0)
--#define TWO RCONST(2.0)
--
--#define DEBUG
--
--/*
-- * =================================================================
-- * PROTOTYPES FOR PRIVATE FUNCTIONS
-- * =================================================================
-- */
--
--
--/* help functions */
--void DenSLU2Vector(N_Vector res, SuperMatrix *x);
--void Vector2DenSLU(N_Vector x, SuperMatrix *res);
--
--
--/* KINPinv linit, lsetup, lsolve, and lfree routines */
--
--static int kinSLUGInit(KINMem kin_mem);
--static int kinSLUGSetup(KINMem kin_mem);
--static int kinSLUGSolve(KINMem kin_mem, N_Vector x, N_Vector b,
-- realtype *res_norm);
--static void kinSLUGFree(KINMem kin_mem);
--
--
--/*
-- * =================================================================
-- * READIBILITY REPLACEMENTS
-- * =================================================================
-- */
--
--#define lrw1 (kin_mem->kin_lrw1)
--#define liw1 (kin_mem->kin_liw1)
--#define func (kin_mem->kin_func)
--#define printfl (kin_mem->kin_printfl)
--#define linit (kin_mem->kin_linit)
--#define lsetup (kin_mem->kin_lsetup)
--#define lsolve (kin_mem->kin_lsolve)
--#define lfree (kin_mem->kin_lfree)
--#define lmem (kin_mem->kin_lmem)
--#define inexact_ls (kin_mem->kin_inexact_ls)
--#define uu (kin_mem->kin_uu)
--#define fval (kin_mem->kin_fval)
--#define uscale (kin_mem->kin_uscale)
--#define fscale (kin_mem->kin_fscale)
--#define sqrt_relfunc (kin_mem->kin_sqrt_relfunc)
--#define sJpnorm (kin_mem->kin_sJpnorm)
--#define sfdotJp (kin_mem->kin_sfdotJp)
--#define errfp (kin_mem->kin_errfp)
--#define infofp (kin_mem->kin_infofp)
--#define setupNonNull (kin_mem->kin_setupNonNull)
--#define vtemp1 (kin_mem->kin_vtemp1)
--#define vec_tmpl (kin_mem->kin_vtemp1)
--#define vtemp2 (kin_mem->kin_vtemp2)
--
--#define mtype (kinpinv_mem->d_type)
--#define n (kinpinv_mem->d_n)
--#define ml (kinpinv_mem->d_ml)
--#define mu (kinpinv_mem->d_mu)
--#define smu (kinpinv_mem->d_smu)
--#define jacDQ (kinpinv_mem->d_jacDQ)
--#define djac (kinpinv_mem->d_djac)
--#define spjac (kinpinv_mem->d_spjac)
--#define J (kinpinv_mem->d_J)
--#define spJ (kinpinv_mem->sp_J)
--#define L (kinpinv_mem->sp_L)
--#define U (kinpinv_mem->sp_U)
--#define B (kinpinv_mem->sp_B)
--#define perm_r (kinpinv_mem->sp_perm_r)
--#define perm_c (kinpinv_mem->sp_perm_c)
--#define spJTJ (kinpinv_mem->sp_JTJ)
--#define options (kinpinv_mem->sp_options)
--#define stat (kinpinv_mem->sp_stat)
--#define pivots (kinpinv_mem->d_pivots)
--#define nje (kinpinv_mem->d_nje)
--#define nfeDQ (kinpinv_mem->d_nfeDQ)
--#define J_data (kinpinv_mem->d_J_data)
--#define last_flag (kinpinv_mem->d_last_flag)
--#define JTJ (kinpinv_mem->d_JTJ)
--#define regularized (kinpinv_mem->d_regularized)
--#define redojac (kinpinv_mem->d_redojac)
--#define beta (kinpinv_mem->d_beta)
--#define reg_param (kinpinv_mem->d_reg_param)
--
--/*
-- * =================================================================
-- * EXPORTED FUNCTIONS
-- * =================================================================
-- */
--
--/*
-- * -----------------------------------------------------------------
-- * KINSLUG
-- * -----------------------------------------------------------------
-- * This routine initializes the memory record and sets various function
-- * fields specific to the sparse regularization solver module.
-- * KINSLUG sets the kin_linit, kin_lsetup, kin_lsolve, kin_lfree fields
-- * in *kinmem to be kinSLUGInit, kinSLUGSetup, kinSLUGSolve, and
-- * kinSLUGFree, respectively.
-- * It allocates memory for a structure of type KINPinvMemRec and sets
-- * the kin_lmem field in *kinmem to the address of this structure.
-- * It sets setupNonNull in *kinmem to TRUE, and the djac field to the
-- * default kinPinvDenseDQJac.
-- * Finally, it allocates memory for J and pivots.
-- *
-- * NOTE: The sparse regularized linear solver assumes a serial implementation
-- * of the NVECTOR package. Therefore, KINSLUG will first
-- * test for compatible a compatible N_Vector internal
-- * representation by checking that N_VGetArrayPointer and
-- * N_VSetArrayPointer exist.
-- * -----------------------------------------------------------------
-- */
--
--int KINSLUG(void *kinmem, int N)
--{
-- KINMem kin_mem;
-- KINPinvMem kinpinv_mem;
-- /* Check if kinmem is different from NULL */
-- if (kinmem == NULL) {
-- KINProcessError(NULL, KINPINV_MEM_NULL, "KINPINV", "KINPinv", MSGD_KINMEM_NULL);
-- return(KINPINV_MEM_NULL);
-- }
-- kin_mem = (KINMem) kinmem;
--
-- /* Test if the NVECTOR package is present */
-- if (vec_tmpl->ops->nvgetarraypointer == NULL ||
-- vec_tmpl->ops->nvsetarraypointer == NULL) {
-- KINProcessError(kin_mem, KINPINV_ILL_INPUT, "KINPINV", "KINPinv", MSGD_BAD_NVECTOR);
-- return(KINPINV_ILL_INPUT);
-- }
--
-- if (lfree !=NULL) lfree(kin_mem);
--
-- /* Set four main function fields in kin_mem */
-- linit = kinSLUGInit;
-- lsetup = kinSLUGSetup;
-- lsolve = kinSLUGSolve;
-- lfree = kinSLUGFree;
--
-- /* Get memory for KINDlsMemRec */
-- kinpinv_mem = NULL;
-- kinpinv_mem = (KINPinvMem) malloc(sizeof(struct KINPinvMemRec));
-- if (kinpinv_mem == NULL) {
-- KINProcessError(kin_mem, KINPINV_MEM_FAIL, "KINSLUG", "KINSLUG", MSGD_MEM_FAIL);
-- return(KINPINV_MEM_FAIL);
-- }
--
-- /* Set matrix type */
-- mtype = SUNDIALS_DENSE;
--
-- /* Set default Jacobian routine and Jacobian data */
-- jacDQ = TRUE;
-- spjac = NULL;
-- J_data = NULL;
-- last_flag = KINPINV_SUCCESS;
--
-- setupNonNull = TRUE;
--
-- /* Set problem dimension */
-- n = N;
--
-- /* Allocate memory for J,JTJ and pivots */
--
-- spJ = NULL;
-- spJ = calloc(1,sizeof(SuperMatrix)) ;
-- /*spJ = (SuperMatrix *) SUPERLU_MALLOC( sizeof(SuperMatrix) );*/
-- if (spJ == NULL) {
-- KINProcessError(kin_mem, KINPINV_MEM_FAIL, "KINSLUG", "KINSLUG", MSGD_MEM_FAIL);
-- free(kinpinv_mem); kinpinv_mem = NULL;
-- return(KINPINV_MEM_FAIL);
-- }
--
-- L = NULL;
-- L = calloc(1,sizeof(SuperMatrix)) ;
-- if (L == NULL) {
-- KINProcessError(kin_mem, KINPINV_MEM_FAIL, "KINSLUG", "KINSLUG", MSGD_MEM_FAIL);
-- free(spJ);
-- free(kinpinv_mem); kinpinv_mem = NULL;
-- return(KINPINV_MEM_FAIL);
-- }
--
-- U = NULL;
-- U = calloc(1,sizeof(SuperMatrix)) ;
-- if (U == NULL) {
-- KINProcessError(kin_mem, KINPINV_MEM_FAIL, "KINSLUG", "KINSLUG", MSGD_MEM_FAIL);
-- free(spJ);
-- free(L);
-- free(kinpinv_mem); kinpinv_mem = NULL;
-- return(KINPINV_MEM_FAIL);
-- }
--
-- B = NULL;
-- B = calloc(1,sizeof(SuperMatrix)) ;
-- if (B == NULL) {
-- KINProcessError(kin_mem, KINPINV_MEM_FAIL, "KINSLUG", "KINSLUG", MSGD_MEM_FAIL);
-- free(spJ);
-- free(L);
-- free(U);
-- free(kinpinv_mem); kinpinv_mem = NULL;
-- return(KINPINV_MEM_FAIL);
-- }
--
-- perm_r = NULL;
-- perm_r = intMalloc(N);
-- if (perm_r == NULL) {
-- KINProcessError(kin_mem, KINPINV_MEM_FAIL, "KINSLUG", "KINSLUG", MSGD_MEM_FAIL);
-- free(spJ);
-- free(L);
-- free(U);
-- free(B);
-- free(kinpinv_mem); kinpinv_mem = NULL;
-- return(KINPINV_MEM_FAIL);
-- }
--
-- perm_c = NULL;
-- perm_c = calloc(N,sizeof(int));
-- if (perm_c == NULL) {
-- KINProcessError(kin_mem, KINPINV_MEM_FAIL, "KINSLUG", "KINSLUG", MSGD_MEM_FAIL);
-- free(spJ);
-- free(L);
-- free(U);
-- free(B);
-- free(perm_r);
-- free(kinpinv_mem); kinpinv_mem = NULL;
-- return(KINPINV_MEM_FAIL);
-- }
--
-- options = NULL;
-- options = (superlu_options_t *) calloc(1,sizeof(superlu_options_t));
-- if (options == NULL) {
-- KINProcessError(kin_mem, KINPINV_MEM_FAIL, "KINSLUG", "KINSLUG", MSGD_MEM_FAIL);
-- free(spJ);
-- free(L);
-- free(U);
-- free(B);
-- free(perm_r);
-- free(perm_c);
-- free(kinpinv_mem); kinpinv_mem = NULL;
-- return(KINPINV_MEM_FAIL);
-- }
--
-- stat = NULL;
-- stat = (SuperLUStat_t *) calloc(1,sizeof(SuperLUStat_t));
-- if (stat == NULL) {
-- KINProcessError(kin_mem, KINPINV_MEM_FAIL, "KINSLUG", "KINSLUG", MSGD_MEM_FAIL);
-- free(spJ);
-- free(L);
-- free(U);
-- free(B);
-- free(perm_r);
-- free(perm_c);
-- free(options);
-- free(kinpinv_mem); kinpinv_mem = NULL;
-- return(KINPINV_MEM_FAIL);
-- }
--
-- spJTJ = NULL;
-- spJTJ= (SuperMatrix *) calloc(1,sizeof(SuperMatrix));
-- if (spJTJ == NULL) {
-- KINProcessError(kin_mem, KINPINV_MEM_FAIL, "KINSLUG", "KINSLUG", MSGD_MEM_FAIL);
-- free(spJ);
-- free(L);
-- free(U);
-- free(B);
-- free(perm_r);
-- free(perm_c);
-- free(options);
-- free(stat);
-- free(kinpinv_mem); kinpinv_mem = NULL;
-- return(KINPINV_MEM_FAIL);
-- }
--
-- if (printfl>0) printf("Sparse data structures created \n");
--
-- /* Set the default input options. */
-- set_default_options(options);
-- options->ColPerm = NATURAL;
-- if (printfl>0) printf("Sparse options set \n");
--
-- /* Initialize the statistics variables. */
-- StatInit(stat);
-- if (printfl>0) printf("Sparse stat initialized \n");
--
-- /* This is a direct linear solver */
-- inexact_ls = FALSE;
--
-- /* Attach linear solver memory to integrator memory */
-- lmem = kinpinv_mem;
--
-- /* Set reg_param 'not set' */
-- reg_param = 0;
--
--
-- return(KINPINV_SUCCESS);
--}
--
--/*
-- * =================================================================
-- * PRIVATE FUNCTIONS
-- * =================================================================
-- */
--
--/*
-- * -----------------------------------------------------------------
-- * kinSLUGInit
-- * -----------------------------------------------------------------
-- * This routine does remaining initializations specific to the Sparse
-- * linear solver.
-- * -----------------------------------------------------------------
-- */
--
--static int kinSLUGInit(KINMem kin_mem)
--{
-- KINPinvMem kinpinv_mem;
--
-- kinpinv_mem = (KINPinvMem) lmem;
--
-- nje = 0;
-- nfeDQ = 0;
--
-- /*
-- * Set where to find jacobian data
-- * if jacDQ=True it will be calculated by finite differenses
-- * otherwise a user-supplied jacobian will be used
-- */
--
--
-- if (jacDQ) {
-- djac = kinPinvDQJac;
-- J_data = kin_mem;
-- } else {
-- J_data = kin_mem->kin_user_data;
-- }
--
-- /* Set regularization parameter */
-- if (reg_param == 0){
-- reg_param = 1;
-- }
-- last_flag = KINPINV_SUCCESS;
-- return(0);
--}
--
--/*
-- * -----------------------------------------------------------------
-- * kinSLUGSetup
-- * -----------------------------------------------------------------
-- * This routine does the setup operations for the linear solver and
-- * prepares J transpose J + h^2 I if necessary for regularization.
-- * -----------------------------------------------------------------
-- */
--
--static int kinSLUGSetup(KINMem kin_mem)
--{
-- KINPinvMem kinpinv_mem;
-- SuperMatrix spJC ; /* Matrix postmultiplied by P_c */
-- int retval,relax,panel_size,*etree,permc_spec;
-- double rp = 0;
--
-- kinpinv_mem = (KINPinvMem) lmem;
--
-- regularized = FALSE;
--
-- /* Calculate value of jacobian */
-- nje++;
--
-- retval = spjac(n, uu, fval, spJ, J_data, vtemp1, vtemp2);
-- if (retval != 0) {
-- last_flag = -1;
-- return(-1);
-- }
--
-- /* Set flag that the matrix needs to be factorized */
--
-- options->Fact = DOFACT;
--
--
-- /* Get column permutation vector perm_c */
-- permc_spec = options->ColPerm;
-- get_perm_c(permc_spec,spJ,perm_c);
--
-- /* Permute Jacobian and calculate and calculate etree */
-- etree = calloc(n,sizeof(int));
--
-- sp_preorder(options, spJ, perm_c, etree, &spJC);
--
-- /* Get sizes for supernodes and panels from SUperLU */
-- panel_size = sp_ienv(1);
-- relax = sp_ienv(2);
--
--
-- /* Try to do a LU factorization of spJ */
-- dgstrf(options, &spJC, relax, panel_size, etree,
-- NULL, 0,perm_c, perm_r, L, U, stat, &retval);
-- if (retval != 0){
-- /* Factorization failed, determine reason */
-- if (retval < 0) {
-- printf("Argument %i has illegal value\n",(-retval));
-- } else if (retval > n) {
-- printf("%i nb of bytes allocated when memory allocation failed \n",(retval+n));
-- } else {
-- /* Jacobian is singular, perform regularization */
-- if (printfl > 0) printf("Jacobian is singular at %i, regularizing \n ",retval);
--
--
-- /* Recalculate jacobian */
--
-- Destroy_CompCol_Matrix(spJ);
-- nje++;
-- retval = spjac(n, uu, fval, spJ, J_data, vtemp1, vtemp2);
-- if (retval != 0) {
-- last_flag = -1;
-- free(etree);
-- return(-1);
-- }
--
-- /* Calculate regularization parameter */
-- /* Put rhs in B2 */
-- B = calloc(1,sizeof(SuperMatrix));
-- Vector2DenSLU(fval,B);
--
-- rp = getRegParam(spJ,B);
--
-- printf("rp: %f \n",rp*rp);
-- if (rp > 1) {
-- reg_param = 1;
-- } else {
-- reg_param = rp;
-- }
-- if (printfl > 0) printf("Regparam: %f, \n ",reg_param);
-- /* Calculate regularized matrix */
-- spJTJ = regSparseMatrix(spJ,sqrt(reg_param));
--
-- /* Get column permutation vector perm_c */
-- permc_spec = options->ColPerm;
-- get_perm_c(permc_spec,spJTJ,perm_c);
--
-- /* Do a preorder on the column etree */
-- sp_preorder(options, spJTJ, perm_c, etree, &spJC);
--
-- /* Get sizes for supernodes and panels from SUperLU */
-- panel_size = sp_ienv(1);
-- relax = sp_ienv(2);
--
--
-- /* Try to do a LU factorization of spJ */
-- dgstrf(options, &spJC, relax, panel_size, etree,
-- NULL, 0,perm_c, perm_r, L, U, stat, &retval);
--
-- regularized = TRUE;
-- if (retval == 0) {
-- if (printfl > 0) printf("Jacobian regularized \n");
-- /* set flag that the matrix is factorized */
-- options->Fact = FACTORED;
-- } else {
-- if (printfl >0) {
-- if (retval > n) {
-- printf("Error in Jacobian regularization, %d nb of bytes allocated when allocation failed. n: %d",retval,n);
-- } else {
-- printf("Error in Jacobian regularization, retval: %d, n: %d,reg_param: %f",retval,n,reg_param);
-- }
-- }
-- }
-- }
--
--
-- } else {
-- /* set flag that the matrix is factorized */
-- options->Fact = FACTORED;
-- fflush(stdout);
-- }
--
-- redojac = FALSE;
-- /* Free allocated memory and return flags */
-- SUPERLU_FREE(etree);
-- Destroy_CompCol_Permuted(&spJC);
--
-- last_flag = retval;
-- if (retval > 0) return(-1);
--
-- return(0);
--}
--
--/*
-- * -----------------------------------------------------------------
-- * Vector2SDenSLU
-- * -----------------------------------------------------------------
-- * Routine used to get the information from a NVector into the structure
-- * needed by SuperLU.
-- * -----------------------------------------------------------------
-- */
--void Vector2DenSLU(N_Vector x, SuperMatrix *res) {
-- realtype *data;
-- int length = NV_LENGTH_S(x);
-- Stype_t S = SLU_DN ;
-- Dtype_t D = SLU_D ;
-- Mtype_t M = SLU_GE;
--
-- /* Get data from the N_Vector */
-- data = N_VGetArrayPointer(x);
--
-- /* Create dense Supermatrix */
-- dCreate_Dense_Matrix(res,length,1,data,length,S,D,M);
--
--}
--
--/*
-- * -----------------------------------------------------------------
-- * DenSLU2Vector
-- * -----------------------------------------------------------------
-- * Routine used to get the information from a SuperMatrix into a
-- * N_Vector.
-- * -----------------------------------------------------------------
-- */
--void DenSLU2Vector(N_Vector res, SuperMatrix *x) {
-- realtype *data;
-- DNformat *storage;
--
-- /* Get data pointer from SuperMatrix */
-- storage = x->Store;
-- data = storage->nzval;
--
-- /* Set data pointer in N_Vector */
-- N_VSetArrayPointer(data,res);
--
--}
--
--/*
-- * -----------------------------------------------------------------
-- * kinSLUGSolve
-- * -----------------------------------------------------------------
-- * This routine handles the solve operation for the sparse linear solver
-- * by calling the sparse backsolve routine. The returned value is 0.
-- * -----------------------------------------------------------------
-- */
--
--static int kinSLUGSolve(KINMem kin_mem, N_Vector x, N_Vector b, realtype *res_norm)
--{
-- KINPinvMem kinpinv_mem;
-- SuperMatrix *B2;
-- int info;
-- kinpinv_mem = (KINPinvMem) lmem;
--
-- if (redojac) {
-- return 1;
-- }
-- /* Copy the right-hand side into x*/
-- N_VScale(ONE, b, x);
--
--
-- if (regularized) {
--
-- /* Put rhs in B2 */
-- B2 = calloc(1,sizeof(SuperMatrix));
-- Vector2DenSLU(x,B2);
--
-- /* Get regularized rhs */
-- B = getRegRHS(spJ,B2);
--
-- regularized = FALSE;
-- redojac = TRUE;
--
-- /* Take out the garbage */
-- free(B2);
-- } else {
-- /* put rhs in B */
-- Vector2DenSLU(x,B);
--
-- }
--
-- /* Back-solve, get solution in B and propagate it to x*/
-- dgstrs (NOTRANS, L, U, perm_c, perm_r, B, stat, &info);
--
-- if (info < 0) {
-- printf("Solving failed with flag %i \n",info);
-- last_flag = info;
--
-- } else {
-- DenSLU2Vector(x,B);
-- }
--
--
--
--
-- /* Compute the terms Jpnorm and sfdotJp for use in the global strategy
-- routines and in KINForcingTerm. Both of these terms are subsequently
-- corrected if the step is reduced by constraints or the line search.
--
-- sJpnorm is the norm of the scaled product (scaled by fscale) of
-- the current Jacobian matrix J and the step vector p.
--
-- sfdotJp is the dot product of the scaled f vector and the scaled
-- vector J*p, where the scaling uses fscale. */
--
-- sJpnorm = N_VWL2Norm(b,fscale);
-- N_VProd(b, fscale, b);
-- N_VProd(b, fscale, b);
-- sfdotJp = N_VDotProd(fval, b);
--
--
-- last_flag = KINPINV_SUCCESS;
--
-- return(0);
--}
--
--/*
-- * -----------------------------------------------------------------
-- * kinSLUGFree
-- * -----------------------------------------------------------------
-- * This routine frees memory specific to the dense linear solver.
-- * -----------------------------------------------------------------
-- */
--
--static void kinSLUGFree(KINMem kin_mem)
--{
-- KINPinvMem kinpinv_mem;
--
-- kinpinv_mem = (KINPinvMem) lmem;
-- /* De-allocate storage */
--
-- SUPERLU_FREE (perm_r);
-- SUPERLU_FREE (perm_c);
-- Destroy_CompCol_Matrix(spJ);
-- Destroy_Dense_Matrix(B);
-- Destroy_SuperNode_Matrix(L);
-- Destroy_CompCol_Matrix(U);
-- Destroy_CompCol_Matrix(spJTJ);
-- StatFree(stat);
-- free(stat);
-- free(options);
-- free(spJ);
-- free(B);
-- free(L);
-- free(U);
-- free(spJTJ);
--
-- free(kinpinv_mem); kinpinv_mem = NULL;
--}
--
-Index: src/lib/kinsol_jmod.h
-===================================================================
---- assimulo/lib/kinsol_jmod.h (revision 838)
-+++ assimulo/lib/kinsol_jmod.h (nonexistent)
-@@ -1,241 +0,0 @@
--/*
-- * This file has been modified by Modelon AB, changes are Copyright
-- * (c) 2014 Modelon AB.
-- *
-- * Original copyright notice:
-- *
-- * Copyright (c) 2002, The Regents of the University of California.
-- * Produced at the Lawrence Livermore National Laboratory.
-- * Written by S.D. Cohen, A.C. Hindmarsh, R. Serban,
-- * D. Shumaker, and A.G. Taylor.
-- * UCRL-CODE-155951 (CVODE)
-- * UCRL-CODE-155950 (CVODES)
-- * UCRL-CODE-155952 (IDA)
-- * UCRL-CODE-237203 (IDAS)
-- * UCRL-CODE-155953 (KINSOL)
-- * All rights reserved.
--
-- * This file is part of SUNDIALS.
--
-- * Redistribution and use in source and binary forms, with or without
-- * modification, are permitted provided that the following conditions
-- * are met:
-- *
-- * 1. Redistributions of source code must retain the above copyright
-- * notice, this list of conditions and the disclaimer below.
-- *
-- * 2. Redistributions in binary form must reproduce the above copyright
-- * notice, this list of conditions and the disclaimer (as noted below)
-- * in the documentation and/or other materials provided with the
-- * distribution.
-- *
-- * 3. Neither the name of the UC/LLNL nor the names of its contributors
-- * may be used to endorse or promote products derived from this software
-- * without specific prior written permission.
-- *
-- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-- * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-- * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
-- * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
-- * REGENTS OF THE UNIVERSITY OF CALIFORNIA, THE U.S. DEPARTMENT OF ENERGY
-- * OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-- * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
-- * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
-- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
-- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-- *
-- * Additional BSD Notice
-- * ---------------------
-- * 1. This notice is required to be provided under our contract with
-- * the U.S. Department of Energy (DOE). This work was produced at the
-- * University of California, Lawrence Livermore National Laboratory
-- * under Contract No. W-7405-ENG-48 with the DOE.
-- *
-- * 2. Neither the United States Government nor the University of
-- * California nor any of their employees, makes any warranty, express
-- * or implied, or assumes any liability or responsibility for the
-- * accuracy, completeness, or usefulness of any information, apparatus,
-- * product, or process disclosed, or represents that its use would not
-- * infringe privately-owned rights.
-- *
-- * 3. Also, reference herein to any specific commercial products,
-- * process, or services by trade name, trademark, manufacturer or
-- * otherwise does not necessarily constitute or imply its endorsement,
-- * recommendation, or favoring by the United States Government or the
-- * University of California. The views and opinions of authors expressed
-- * herein do not necessarily state or reflect those of the United States
-- * Government or the University of California, and shall not be used for
-- * advertising or product endorsement purposes.
-- */
--
--
--#ifndef _KINJMOD_H
--#define _KINJMOD_H
--
--#ifdef __cplusplus /* wrapper to enable C++ usage */
--extern "C" {
--#endif
--
--#include <sundials/sundials_direct.h>
--#include <sundials/sundials_nvector.h>
--
--/*
-- * =================================================================
-- * K I N P I N V C O N S T A N T S
-- * =================================================================
-- */
--
--/*
-- * -----------------------------------------------------------------
-- * KINPINV return values
-- * -----------------------------------------------------------------
-- */
--
--#define KINPINV_SUCCESS 0
--#define KINPINV_MEM_NULL -1
--#define KINPINV_LMEM_NULL -2
--#define KINPINV_ILL_INPUT -3
--#define KINPINV_MEM_FAIL -4
--
--/* Additional last_flag values */
--
--#define KINPINV_JACFUNC_UNRECVR -5
--#define KINPINV_JACFUNC_RECVR -6
--
--/*
-- * =================================================================
-- * F U N C T I O N T Y P E S
-- * =================================================================
-- */
--
--/*
-- * -----------------------------------------------------------------
-- * Type: KINPinvJacFn
-- * -----------------------------------------------------------------
-- *
-- * A dense Jacobian approximation function Jac must be of type
-- * KINDlsDenseJacFn. Its parameters are:
-- *
-- * N - problem size.
-- *
-- * u - current iterate (unscaled) [input]
-- *
-- * fu - vector (type N_Vector) containing result of nonlinear
-- * system function evaluated at current iterate:
-- * fu = F(u) [input]
-- *
-- * J - dense matrix (of type DlsMat) that will be loaded
-- * by a KINDlsDenseJacFn with an approximation to the
-- * Jacobian matrix J = (dF_i/dy_j).
-- *
-- * user_data - pointer to user data - the same as the user_data
-- * parameter passed to KINSetFdata.
-- *
-- * tmp1, tmp2 - available scratch vectors (volatile storage)
-- *
-- * A KINPinvJacFn should return 0 if successful, a positive
-- * value if a recoverable error occurred, and a negative value if
-- * an unrecoverable error occurred.
-- *
-- * -----------------------------------------------------------------
-- *
-- * NOTE: The following are two efficient ways to load a dense Jac:
-- * (1) (with macros - no explicit data structure references)
-- * for (j=0; j < Neq; j++) {
-- * col_j = DENSE_COL(Jac,j);
-- * for (i=0; i < Neq; i++) {
-- * generate J_ij = the (i,j)th Jacobian element
-- * col_j[i] = J_ij;
-- * }
-- * }
-- * (2) (without macros - explicit data structure references)
-- * for (j=0; j < Neq; j++) {
-- * col_j = (Jac->data)[j];
-- * for (i=0; i < Neq; i++) {
-- * generate J_ij = the (i,j)th Jacobian element
-- * col_j[i] = J_ij;
-- * }
-- * }
-- * A third way, using the DENSE_ELEM(A,i,j) macro, is much less
-- * efficient in general. It is only appropriate for use in small
-- * problems in which efficiency of access is NOT a major concern.
-- *
-- * -----------------------------------------------------------------
-- */
--
--
--typedef int (*KINPinvJacFn)(int N,
-- N_Vector u, N_Vector fu,
-- DlsMat J, void *user_data,
-- N_Vector tmp1, N_Vector tmp2);
--
--
--/*
-- * =================================================================
-- * E X P O R T E D F U N C T I O N S
-- * =================================================================
-- */
--
--/*
-- * -----------------------------------------------------------------
-- * Optional inputs to the KINPinv linear solver
-- * -----------------------------------------------------------------
-- *
-- * KINDlsSetDenseJacFn specifies the dense Jacobian approximation
-- * routine to be used for a direct dense linear solver.
-- *
-- * By default, a difference quotient approximation, supplied with
-- * the solver is used.
-- *
-- * The return value is one of:
-- * KINPINV_SUCCESS if successful
-- * KINPINV_MEM_NULL if the KINSOL memory was NULL
-- * KINPINV_LMEM_NULL if the linear solver memory was NULL
-- * -----------------------------------------------------------------
-- */
--
--SUNDIALS_EXPORT int KINPinvSetJacFn(void *kinmem, KINPinvJacFn jac);
--
--/*
-- Set regularization parameter
-- */
--SUNDIALS_EXPORT int KINPinvSetRegParam(void *kinmem, realtype reg_p);
--
--/*
-- * -----------------------------------------------------------------
-- * Optional outputs from a KINDLS linear solver
-- * -----------------------------------------------------------------
-- *
-- * KINPinvGetWorkSpace returns the real and integer workspace used
-- * by the KINDLS linear solver.
-- * KINPinvGetNumJacEvals returns the number of calls made to the
-- * Jacobian evaluation routine.
-- * KINPinvGetNumFuncEvals returns the number of calls to the user's F
-- * routine due to finite difference Jacobian
-- * evaluation.
-- * KINPinvGetLastFlag returns the last error flag set by any of
-- * the KINDLS interface functions.
-- * KINPinvGetReturnFlagName returns the name of the constant
-- * associated with a KINDLS return flag
-- *
-- * The return value of KINPinvGet* is one of:
-- * KINPINV_SUCCESS if successful
-- * KINPINV_MEM_NULL if the KINSOL memory was NULL
-- * KINPINV_LMEM_NULL if the linear solver memory was NULL
-- * -----------------------------------------------------------------
-- */
--
--
--SUNDIALS_EXPORT int KINPinvGetWorkSpace(void *kinmem, long int *lenrwB, long int *leniwB);
--SUNDIALS_EXPORT int KINPinvGetNumJacEvals(void *kinmem, long int *njevalsB);
--SUNDIALS_EXPORT int KINPinvGetNumFuncEvals(void *kinmem, long int *nfevalsB);
--SUNDIALS_EXPORT int KINPinvGetLastFlag(void *kinmem, int *flag);
--SUNDIALS_EXPORT char *KINPinvGetReturnFlagName(int flag);
--
--#ifdef __cplusplus
--}
--#endif
--
--#endif
-Index: src/lib/kinpinv.c
-===================================================================
---- assimulo/lib/kinpinv.c (revision 838)
-+++ assimulo/lib/kinpinv.c (nonexistent)
-@@ -1,578 +0,0 @@
--/*
-- * This file has been modified by Modelon AB, changes are Copyright
-- * (c) 2014 Modelon AB.
-- *
-- * Original copyright notice:
-- *
-- * Copyright (c) 2002, The Regents of the University of California.
-- * Produced at the Lawrence Livermore National Laboratory.
-- * Written by S.D. Cohen, A.C. Hindmarsh, R. Serban,
-- * D. Shumaker, and A.G. Taylor.
-- * UCRL-CODE-155951 (CVODE)
-- * UCRL-CODE-155950 (CVODES)
-- * UCRL-CODE-155952 (IDA)
-- * UCRL-CODE-237203 (IDAS)
-- * UCRL-CODE-155953 (KINSOL)
-- * All rights reserved.
--
-- * This file is part of SUNDIALS.
--
-- * Redistribution and use in source and binary forms, with or without
-- * modification, are permitted provided that the following conditions
-- * are met:
-- *
-- * 1. Redistributions of source code must retain the above copyright
-- * notice, this list of conditions and the disclaimer below.
-- *
-- * 2. Redistributions in binary form must reproduce the above copyright
-- * notice, this list of conditions and the disclaimer (as noted below)
-- * in the documentation and/or other materials provided with the
-- * distribution.
-- *
-- * 3. Neither the name of the UC/LLNL nor the names of its contributors
-- * may be used to endorse or promote products derived from this software
-- * without specific prior written permission.
-- *
-- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-- * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-- * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
-- * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
-- * REGENTS OF THE UNIVERSITY OF CALIFORNIA, THE U.S. DEPARTMENT OF ENERGY
-- * OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-- * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
-- * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
-- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
-- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-- *
-- * Additional BSD Notice
-- * ---------------------
-- * 1. This notice is required to be provided under our contract with
-- * the U.S. Department of Energy (DOE). This work was produced at the
-- * University of California, Lawrence Livermore National Laboratory
-- * under Contract No. W-7405-ENG-48 with the DOE.
-- *
-- * 2. Neither the United States Government nor the University of
-- * California nor any of their employees, makes any warranty, express
-- * or implied, or assumes any liability or responsibility for the
-- * accuracy, completeness, or usefulness of any information, apparatus,
-- * product, or process disclosed, or represents that its use would not
-- * infringe privately-owned rights.
-- *
-- * 3. Also, reference herein to any specific commercial products,
-- * process, or services by trade name, trademark, manufacturer or
-- * otherwise does not necessarily constitute or imply its endorsement,
-- * recommendation, or favoring by the United States Government or the
-- * University of California. The views and opinions of authors expressed
-- * herein do not necessarily state or reflect those of the United States
-- * Government or the University of California, and shall not be used for
-- * advertising or product endorsement purposes.
-- */
--
--/*
-- *This is a linear solver using Tikhonov regularization
-- */
--#include <stdio.h>
--#include <stdlib.h>
--#include <math.h>
--
--#include "kinsol_jmod_impl.h"
--#include "kinpinv.h"
--#include "kinsol/kinsol_impl.h"
--
--#include <sundials/sundials_direct.h>
--#include <nvector/nvector_serial.h>
--#include <sundials/sundials_math.h>
--
--/* Constants */
--
--#define ZERO RCONST(0.0)
--#define ONE RCONST(1.0)
--#define TWO RCONST(2.0)
--
--/*
-- * =================================================================
-- * PROTOTYPES FOR PRIVATE FUNCTIONS
-- * =================================================================
-- */
--
--
--/* help functions */
--void regMatrix(realtype **JTJ_c, realtype **jac, realtype h,int size);
--
--/* KINPinv linit, lsetup, lsolve, and lfree routines */
--
--static int kinPinvInit(KINMem kin_mem);
--static int kinPinvSetup(KINMem kin_mem);
--static int kinPinvSolve(KINMem kin_mem, N_Vector x, N_Vector b,
-- realtype *res_norm);
--static void kinPinvFree(KINMem kin_mem);
--
--
--/*
-- * =================================================================
-- * READIBILITY REPLACEMENTS
-- * =================================================================
-- */
--
--#define lrw1 (kin_mem->kin_lrw1)
--#define liw1 (kin_mem->kin_liw1)
--#define func (kin_mem->kin_func)
--#define printfl (kin_mem->kin_printfl)
--#define linit (kin_mem->kin_linit)
--#define lsetup (kin_mem->kin_lsetup)
--#define lsolve (kin_mem->kin_lsolve)
--#define lfree (kin_mem->kin_lfree)
--#define lmem (kin_mem->kin_lmem)
--#define inexact_ls (kin_mem->kin_inexact_ls)
--#define uu (kin_mem->kin_uu)
--#define fval (kin_mem->kin_fval)
--#define uscale (kin_mem->kin_uscale)
--#define fscale (kin_mem->kin_fscale)
--#define sqrt_relfunc (kin_mem->kin_sqrt_relfunc)
--#define sJpnorm (kin_mem->kin_sJpnorm)
--#if SUNDIALS_26
-- #define sFdotJp (kin_mem->kin_sFdotJp)
--#else
-- #define sfdotJp (kin_mem->kin_sfdotJp)
--#endif
--#define errfp (kin_mem->kin_errfp)
--#define infofp (kin_mem->kin_infofp)
--#define setupNonNull (kin_mem->kin_setupNonNull)
--#define vtemp1 (kin_mem->kin_vtemp1)
--#define vec_tmpl (kin_mem->kin_vtemp1)
--#define vtemp2 (kin_mem->kin_vtemp2)
--
--#define mtype (kinpinv_mem->d_type)
--#define n (kinpinv_mem->d_n)
--#define ml (kinpinv_mem->d_ml)
--#define mu (kinpinv_mem->d_mu)
--#define smu (kinpinv_mem->d_smu)
--#define jacDQ (kinpinv_mem->d_jacDQ)
--#define djac (kinpinv_mem->d_djac)
--#define J (kinpinv_mem->d_J)
--#define pivots (kinpinv_mem->d_pivots)
--#define nje (kinpinv_mem->d_nje)
--#define nfeDQ (kinpinv_mem->d_nfeDQ)
--#define J_data (kinpinv_mem->d_J_data)
--#define last_flag (kinpinv_mem->d_last_flag)
--#define JTJ (kinpinv_mem->d_JTJ)
--#define regularized (kinpinv_mem->d_regularized)
--
--#define redojac (kinpinv_mem->d_redojac)
--#define beta (kinpinv_mem->d_beta)
--#define reg_param (kinpinv_mem->d_reg_param)
--
--#define ihfun (kin_mem->kin_ihfun)
--#define ih_data (kin_mem->kin_ih_data)
--#define ehfun (kin_mem->kin_ehfun)
--#define eh_data (kin_mem->kin_eh_data)
--
--/*
-- * =================================================================
-- * EXPORTED FUNCTIONS
-- * =================================================================
-- */
--
--/*
-- * -----------------------------------------------------------------
-- * KINPinv
-- * -----------------------------------------------------------------
-- * This routine initializes the memory record and sets various function
-- * fields specific to the dense pseude-inverse linear solver module.
-- * KINPinv sets the kin_linit, kin_lsetup, kin_lsolve, kin_lfree fields
-- * in *kinmem to be kinPinvInit, kinPinvSetup, kinPinvSolve, and
-- * kinPinvFree, respectively.
-- * It allocates memory for a structure of type KINDlsMemRec and sets
-- * the kin_lmem field in *kinmem to the address of this structure.
-- * It sets setupNonNull in *kinmem to TRUE, and the djac field to the
-- * default kinDlsDenseDQJac.
-- * Finally, it allocates memory for J and pivots.
-- *
-- * NOTE: The dense pseudo-inverse linear solver assumes a serial implementation
-- * of the NVECTOR package. Therefore, KINPinv will first
-- * test for compatible a compatible N_Vector internal
-- * representation by checking that N_VGetArrayPointer and
-- * N_VSetArrayPointer exist.
-- * -----------------------------------------------------------------
-- */
--
--int KINPinv(void *kinmem, int N)
--{
-- KINMem kin_mem;
-- KINPinvMem kinpinv_mem;
-- /* Check if kinmem is different from NULL */
-- if (kinmem == NULL) {
-- KINProcessError(NULL, KINPINV_MEM_NULL, "KINPINV", "KINPinv", MSGD_KINMEM_NULL);
-- return(KINPINV_MEM_NULL);
-- }
-- kin_mem = (KINMem) kinmem;
--
-- /* Test if the NVECTOR package is present */
-- if (vec_tmpl->ops->nvgetarraypointer == NULL ||
-- vec_tmpl->ops->nvsetarraypointer == NULL) {
-- KINProcessError(kin_mem, KINPINV_ILL_INPUT, "KINPINV", "KINPinv", MSGD_BAD_NVECTOR);
-- return(KINPINV_ILL_INPUT);
-- }
--
-- if (lfree !=NULL) lfree(kin_mem);
--
-- /* Set four main function fields in kin_mem */
-- linit = kinPinvInit;
-- lsetup = kinPinvSetup;
-- lsolve = kinPinvSolve;
-- lfree = kinPinvFree;
--
-- /* Get memory for KINDlsMemRec */
-- kinpinv_mem = NULL;
-- kinpinv_mem = (KINPinvMem) malloc(sizeof(struct KINPinvMemRec));
-- if (kinpinv_mem == NULL) {
-- KINProcessError(kin_mem, KINPINV_MEM_FAIL, "KINPINV", "KINPinv", MSGD_MEM_FAIL);
-- return(KINPINV_MEM_FAIL);
-- }
--
-- /* Set matrix type */
-- mtype = SUNDIALS_DENSE;
--
-- /* Set default Jacobian routine and Jacobian data */
-- jacDQ = TRUE;
-- djac = NULL;
-- J_data = NULL;
-- last_flag = KINPINV_SUCCESS;
--
-- setupNonNull = TRUE;
--
-- /* Set problem dimension */
-- n = N;
--
-- /* Allocate memory for J,RTR and pivots */
--
-- J = NULL;
-- J = NewDenseMat(N, N);
-- if (J == NULL) {
-- KINProcessError(kin_mem, KINPINV_MEM_FAIL, "KINPINV", "KINPinv", MSGD_MEM_FAIL);
-- free(kinpinv_mem); kinpinv_mem = NULL;
-- return(KINPINV_MEM_FAIL);
-- }
--
-- JTJ = NULL;
-- JTJ = NewDenseMat(n, n);
-- if (JTJ == NULL) {
-- KINProcessError(kin_mem, KINPINV_MEM_FAIL, "KINPINV", "KINPinv", MSGD_MEM_FAIL);
-- DestroyMat(J);
-- free(kinpinv_mem); kinpinv_mem = NULL;
-- return(KINPINV_MEM_FAIL);
-- }
--
-- pivots = NULL;
-- pivots = NewIntArray(N);
-- if (pivots == NULL) {
-- KINProcessError(kin_mem, KINPINV_MEM_FAIL, "KINPINV", "KINPinv", MSGD_MEM_FAIL);
-- DestroyMat(J);
-- DestroyMat(JTJ);
-- free(kinpinv_mem); kinpinv_mem = NULL;
-- return(KINPINV_MEM_FAIL);
-- }
--
-- beta = NULL;
-- beta = NewRealArray(N);
-- if (beta == NULL) {
-- KINProcessError(kin_mem, KINPINV_MEM_FAIL, "KINPINV", "KINPinv", MSGD_MEM_FAIL);
-- DestroyMat(J);
-- DestroyMat(JTJ);
-- DestroyArray(pivots);
-- free(kinpinv_mem); kinpinv_mem = NULL;
-- return(KINPINV_MEM_FAIL);
-- }
--
-- /* This is a direct linear solver */
-- inexact_ls = FALSE;
--
-- /* Attach linear solver memory to integrator memory */
-- lmem = kinpinv_mem;
-- /* Set reg_param 'not set' */
-- reg_param = 0;
-- redojac=1 ;
-- regularized = FALSE;
-- nje = 0;
-- nfeDQ = 0;
--
-- return(KINPINV_SUCCESS);
--}
--
--/*
-- * =================================================================
-- * PRIVATE FUNCTIONS
-- * =================================================================
-- */
--
--/*
-- * -----------------------------------------------------------------
-- * kinPinvInit
-- * -----------------------------------------------------------------
-- * This routine does remaining initializations specific to the dense
-- * linear solver.
-- * -----------------------------------------------------------------
-- */
--
--static int kinPinvInit(KINMem kin_mem)
--{
-- KINPinvMem kinpinv_mem;
--
-- kinpinv_mem = (KINPinvMem) lmem;
--
-- nje = 0;
-- nfeDQ = 0;
--
-- /*
-- * Set where to find jacobian data
-- * if jacDQ=True it will be calculated by finite differenses
-- * otherwise a user-supplied jacobian will be used
-- */
--
-- if (jacDQ) {
-- djac = kinPinvDQJac;
-- J_data = kin_mem;
-- } else {
-- J_data = kin_mem->kin_user_data;
-- }
--
-- /* Set regularization parameter */
-- if (reg_param == 0){
-- reg_param = 1;
-- }
--
-- last_flag = KINPINV_SUCCESS;
-- return(0);
--}
--
--
--
--/*
-- * Function that calculates the regularized matrix for a given h
-- * The result is returned in res while h is the regularization parameter
-- * and J is the current prblem jacobian.
-- */
--
--void regMatrix(realtype **JTJ_c, realtype **jac, realtype h, int size)
--{
-- int i,j,k;
--
--
-- i = 0;
-- j = 0;
-- k = 0;
-- for (i=0;i<size;i++) {
-- for (j=0;j<size;j++){
--
-- /*Calculate value at RTR(i,j) */
-- JTJ_c[j][i] = 0;
-- for (k=0;k<size;k++) JTJ_c[j][i] += jac[j][k]*jac[i][k];
--
-- /* add the regularization parameter on the diagonal */
-- if (i==j)JTJ_c[j][i] += h*h;
-- }
-- }
--}
--
--/*
-- * -----------------------------------------------------------------
-- * kinPinvSetup
-- * -----------------------------------------------------------------
-- * This routine does the setup operations for the linear solver and
-- * prepares J transpose J + h^2 I if necessary for regularization.
-- * -----------------------------------------------------------------
-- */
--
--static int kinPinvSetup(KINMem kin_mem)
--{
-- KINPinvMem kinpinv_mem;
-- long int ier;
-- int retval;
-- int i,j;
--
-- realtype **JTJ_c ;
-- realtype **jac ;
-- realtype rp ;
-- double data;
-- realtype *b;
--
-- char msg[200];
--
-- kinpinv_mem = (KINPinvMem) lmem;
--
-- /* Calculate value of jacobian */
-- nje++;
-- SetToZero(J);
-- retval = djac(n, uu, fval, J, J_data, vtemp1, vtemp2);
-- if (retval != 0) {
-- ehfun(KIN_LSETUP_FAIL,"KINPINV", "kinPinvSetup", "Jacobian evaluation function failed", eh_data);
-- last_flag = -1;
-- return(-1);
-- }
--
-- /* Try to do a LU factorization of J */
-- ier = DenseGETRF(J, (long int*)pivots);
--
-- /* If the LU factorization failed, perform regularization */
-- if (ier > 0) {
-- /* Calculate value of jacobian */
-- SetToZero(J);
--
-- /* SetToZero(pivots);*/
-- for (i=0; i<n; i++) pivots[i] = 0;
-- nje++;
-- retval = djac(n, uu, fval, J, J_data, vtemp1, vtemp2);
-- if (retval != 0) {
-- ehfun(KIN_LSETUP_FAIL, "KINPINV", "kinPinvSetup", "Jacobian evaluation function failed", eh_data);
-- last_flag = -1;
-- return(-1);
-- }
--
-- /* Calculate J tranpose J */
-- SetToZero(JTJ);
--
-- /* Calculate the regularization parameter */
--
-- jac = J->cols;
-- b = N_VGetArrayPointer(fval);
-- rp = 0;
-- for (i = 0;i<n;i++) {
-- data = 0;
-- for(j = 0;j<n;j++) data += jac[i][j]*b[j];
-- rp += data*data ;
-- }
-- /*printf("rp: %f \n",rp);*/
-- if (sqrt(rp)<1) {
-- reg_param = sqrt(rp);
-- } else {
-- reg_param = 1;
-- }
-- if (printfl > 0) {
-- sprintf(msg, "Singular jacobian detected, regparam: %f",reg_param);
-- ihfun("KINPINV", "kinPinvSetup", msg, ih_data);
-- }
-- /* calculate a regularized matrix */
-- JTJ_c = JTJ->cols;
--
-- regMatrix(JTJ_c,jac,reg_param,n);
--
-- /* LU-factorize the regularized matrix*/
-- ier = DenseGETRF(JTJ,(long int*)pivots);
-- regularized = TRUE;
-- }
-- else {
-- regularized = FALSE;
-- }
--
-- redojac = FALSE;
-- /* Return 0 if the LU was complete; otherwise return -1 */
-- last_flag = ier;
-- if (ier > 0) return(-1);
--
-- return(0);
--}
--
--/*
-- * -----------------------------------------------------------------
-- * kinPinvSolve
-- * -----------------------------------------------------------------
-- * This routine handles the solve operation for the dense linear solver
-- * by calling the dense backsolve routine. The returned value is 0.
-- * -----------------------------------------------------------------
-- */
--
--static int kinPinvSolve(KINMem kin_mem, N_Vector x, N_Vector b, realtype *res_norm)
--{
-- KINPinvMem kinpinv_mem;
-- realtype **jac;
-- realtype *xd;
-- realtype *bx;
-- int i,j;
--
-- kinpinv_mem = (KINPinvMem) lmem;
--
-- if (redojac) {
-- return(1);
-- }
--
-- if (regularized) {
-- if (printfl > 0)
-- ihfun("KINPINV", "kinPinvSetup", "Solving regularized problem", ih_data);
-- /* Calculate new right hand side b = J transpose * b */
-- bx = N_VGetArrayPointer(b);
-- xd = N_VGetArrayPointer(x);
-- jac = J->cols;
-- i=0;
-- j=0;
-- for (i=0;i<n;i++){
-- xd[i] = 0;
-- for (j=0;j<n;j++) xd[i] += jac[i][j]*bx[j];
-- }
-- /* Back-solve and get solution in x */
--
-- /*N_VScale(ONE,x,b);*/
--
--
-- DenseGETRS(JTJ, (long int*)pivots, xd);
--
-- /* Calculate a fresh jacobian - not really needed */
-- redojac = TRUE;
-- } else {
--
-- /* Copy the right-hand side into x */
--
-- N_VScale(ONE, b, x);
-- xd = N_VGetArrayPointer(x);
--
-- /* Back-solve and get solution in x */
-- DenseGETRS(J, (long int*)pivots, xd);
--
-- }
--
-- /* Compute the terms Jpnorm and sfdotJp for use in the global strategy
-- routines and in KINForcingTerm. Both of these terms are subsequently
-- corrected if the step is reduced by constraints or the line search.
--
-- sJpnorm is the norm of the scaled product (scaled by fscale) of
-- the current Jacobian matrix J and the step vector p.
--
-- sfdotJp is the dot product of the scaled f vector and the scaled
-- vector J*p, where the scaling uses fscale. */
--
--
-- sJpnorm = N_VWL2Norm(b,fscale);
-- N_VProd(b, fscale, b);
-- N_VProd(b, fscale, b);
--#if SUNDIALS_26
-- sFdotJp = N_VDotProd(fval, b);
--#else
-- sfdotJp = N_VDotProd(fval, b);
--#endif
--
-- last_flag = KINPINV_SUCCESS;
--
-- return(0);
--}
--
--/*
-- * -----------------------------------------------------------------
-- * kinPinvFree
-- * -----------------------------------------------------------------
-- * This routine frees memory specific to the dense linear solver.
-- * -----------------------------------------------------------------
-- */
--
--static void kinPinvFree(KINMem kin_mem)
--{
-- KINPinvMem kinpinv_mem;
-- kinpinv_mem = (KINPinvMem) lmem;
--
-- DestroyMat(J);
-- DestroyMat(JTJ);
-- DestroyArray(pivots);
-- DestroyArray(beta);
-- free(kinpinv_mem); kinpinv_mem = NULL;
--}
--
-Index: src/lib/sundials_kinsol_core_wSLU.pyx
-===================================================================
---- assimulo/lib/sundials_kinsol_core_wSLU.pyx (revision 838)
-+++ assimulo/lib/sundials_kinsol_core_wSLU.pyx (nonexistent)
-@@ -1,413 +0,0 @@
--#!/usr/bin/env python
--# -*- coding: utf-8 -*-
--
--# Copyright (C) 2010 Modelon AB
--#
--# This program is free software: you can redistribute it and/or modify
--# it under the terms of the GNU Lesser General Public License as published by
--# the Free Software Foundation, version 3 of the License.
--#
--# This program is distributed in the hope that it will be useful,
--# but WITHOUT ANY WARRANTY; without even the implied warranty of
--# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
--# GNU Lesser General Public License for more details.
--#
--# You should have received a copy of the GNU Lesser General Public License
--# along with this program. If not, see <http://www.gnu.org/licenses/>.
--
--"""
--Cython Wrapper for interfacing Python with KINSOL (Sundials Version 2.4.0)
--Johan Ylikiiskilä Modelon AB
--
--see also Jon Olav Vik:
--http://codespeak.net/pipermail/cython-dev/2009-June/005947.html
--
--"""
--from __future__ import division
--
--import scipy.sparse as ss
--
--include "sundials_kinsol_core_wSLU.pxi" # Includes fcts from other header files
--include "sundials_kinsol_core.pxi" # Includes the constants (textual include)
--
--
--cdef int kin_res(N_Vector xv, N_Vector fval, void *problem_data):
-- """
-- Residual fct called by KINSOL
-- """
--
-- cdef:
-- int i
-- ProblemData pData = <ProblemData>problem_data
-- ndarray[realtype, ndim = 1, mode = 'c'] rhs # Container holding return from user fct
--
-- ndarray x = nv2arr(xv)
-- realtype* resptr = (<N_VectorContent_Serial>fval.content).data
--
-- try:
-- rhs = (<object>pData.RHS)(x)
-- for i in range(pData.dim):
--
-- resptr[i] = rhs[i]
--
-- return KIN_SUCCESS
-- except:
-- return KIN_REC_ERR
--
--cdef int kin_jac(int Neq, N_Vector xv, N_Vector fval, DlsMat Jacobian,
-- void *problem_data, N_Vector tmp1, N_Vector tmp2):
-- """
-- This method is used to connect the Assimulo.Problem.jac to the Sundials
-- Jacobian function.
-- """
-- cdef:
-- ProblemData pData = <ProblemData>problem_data
-- realtype* col_i=DENSE_COL(Jacobian,0)
-- ndarray x = nv2arr(xv)
--
-- try:
--
-- jac=(<object>pData.JAC)(x)
-- #This needs further investigations:
-- #memcpy(Jacobian.data,<realtype*>jac.data, pData.memSizeJac)
--
-- for i in range(Neq):
-- col_i = DENSE_COL(Jacobian, i)
-- for j in range(Neq):
-- col_i[j] = jac[j,i]
--
-- return KINDLS_SUCCESS
-- except:
-- return KINDLS_JACFUNC_RECVR #Recoverable Error (See Sundials description)
--
--
--cdef int kin_sp_jac(int Neq, N_Vector xv, N_Vector fval, SuperMatrix *Jacobian,
-- void *problem_data, N_Vector tmp1, N_Vector tmp2):
-- """
-- This method is used to connect the Assimulo.Problem.sparsejac to the KINSOL
-- Jacobian function.
-- """
-- cdef:
-- ProblemData pData = <ProblemData>problem_data
-- ndarray x = nv2arr(xv)
--
-- int m,n,nnz,i
-- realtype* nzval = NULL
-- int* rowind = NULL
-- int* colptr = NULL
-- Stype_t S = SLU_NC
-- Dtype_t D = SLU_D
-- Mtype_t M = SLU_GE
--
-- try:
-- # Get jacobian and extract data
-- jac=(<object>pData.JAC)(x)
-- nnz = jac.nnz
-- m = jac.shape[0]
-- n = jac.shape[1]
-- nzval = <realtype*>calloc(len(jac.data),sizeof(realtype))
-- rowind = <int*>calloc(len(jac.indices),sizeof(int))
-- colptr = <int*>calloc(len(jac.indptr),sizeof(int))
-- arr2realtype(jac.data,nzval,len(jac.data))
-- arr2int(jac.indices,rowind,len(jac.indices))
-- arr2int(jac.indptr,colptr,len(jac.indptr))
--
-- # Create the necessary matrix
-- dCreate_CompCol_Matrix(Jacobian, m, n, nnz, nzval, rowind, colptr, S, D, M)
--
--
-- return KINDLS_SUCCESS
-- except:
-- return KINDLS_JACFUNC_RECVR #Recoverable Error (See Sundials description)
--
--
--class KINError(Exception):
-- """
-- Kinsol exception
-- """
--
-- msg = {KIN_MEM_FAIL : 'Memory allocation failed.',
-- KIN_MEM_NULL : 'KINSOL not properly created.',
-- KIN_ILL_INPUT: 'There was an input error.',
-- KIN_NO_MALLOC: 'Memory not allocated, call KINSOL_init(...)',
-- KIN_LINESEARCH_NONCONV: 'Linesearch could not find a step big enough.',
-- KIN_MAXITER_REACHED: 'Max number of iterations reached',
-- KIN_MXNEWT_5X_EXCEEDED: 'Max size of newton step exceeded 5 times.',
-- KIN_LINESEARCH_BCFAIL: 'Linesearch beta-test failed, probably because of too poor progress.',
-- KIN_LINSOLV_NO_RECOVERY: 'psolve encountered an error, but preconditioner is current',
-- KIN_LINIT_FAIL: 'Init of linear solver failed.',
-- KIN_LSETUP_FAIL: 'pset (preconditioner setup fct) encountered an error',
-- KIN_LSOLVE_FAIL: 'Error in either psolve or in linear solver routine.',
-- KIN_SYSFUNC_FAIL: 'Call to RHS failed.',
-- KIN_FIRST_SYSFUNC_ERR: 'Call to RHS failed on first call',
-- KIN_REPTD_SYSFUNC_ERR: 'Call to RHS failed multiple times.',
-- KIN_STEP_LT_STPTOL: 'Scaled step length too small. Either an approximate solution or a local minimum is reached. Check value of residual.'}
-- value = 0
-- def __init__(self, value):
-- self.value = value
--
-- def __str__(self):
-- try:
-- return repr(self.msg[self.value])
-- except KeyError:
-- return repr('KINSOL failed with flag %s.'%(self.value))
--
--cdef class KINSOL_wrap:
--
-- cdef:
-- void* solver
-- ProblemData pData # A struct containing problem data
-- #ProblemData *ppData # Pointer to above mentioned struct
-- N_Vector x_cur,x_init, x_scale, f_scale, con_nv
-- booleantype noInitSetup, sparse
-- realtype norm_of_res,reg_param
-- ndarray con
-- int print_level
-- ndarray fscale
--
-- def __cinit__(self):
-- self.pData = ProblemData() #Create a new problem struct
-- #self.ppData = &self.pData
--
-- cpdef KINSOL_set_problem_info(self, RHS, dim, JAC= None):
-- """
-- Sets the problem info to the desired struct
-- """
--
-- # Sets residual function
-- self.pData.RHS = <void*>RHS
-- self.pData.dim = dim
-- if JAC != None:
-- self.pData.JAC = <void*>JAC
-- else:
-- self.pData.JAC = NULL
--
--
-- def KINSOL_init(self,RHS,x0,dim, JAC = None, con = None,sparse = False, print_level = 0,norm_of_res = 0,reg_param=0,fscale=None):
-- """
-- Initializes solver
-- """
-- cdef int flag
-- # Set problem info
-- self.noInitSetup = False
-- self.print_level = print_level
-- self.norm_of_res = norm_of_res
-- self.reg_param = reg_param
-- self.KINSOL_set_problem_info(RHS,dim,JAC)
-- self.con = con
-- self.sparse = sparse
-- self.fscale = fscale
--
--
-- # Create initial guess from the supplied numpy array
-- # print "x0 got in KINSOL wrapper: ", x0
-- self.x_cur = arr2nv(x0)
-- self.x_init = arr2nv(x0)
--
--
-- if self.solver == NULL: # solver runs for the first time
--
-- # Create solver
-- self.solver = KINCreate()
-- if self.solver == NULL:
-- raise KINError(KIN_MEM_FAIL)
-- if self.print_level >0:
-- print "KINSOL created"
--
-- # Stop solver from performing precond setup
-- flag = KINSetNoInitSetup(self.solver, self.noInitSetup)
-- if flag < 0:
-- raise KINError(flag)
--
-- flag = KINSetPrintLevel(self.solver, self.print_level)
-- if flag < 0:
-- raise KINError(flag)
--
-- if self.norm_of_res >0:
-- flag = KINSetMaxNewtonStep(self.solver, self.norm_of_res)
-- if flag < 0:
-- raise KINError(flag)
-- else:
-- flag = KINSetMaxNewtonStep(self.solver, 1e20)
-- if flag < 0:
-- raise KINError(flag)
--
-- # If the user has specified constraints, connect them
-- if self.con != None:
-- if self.print_level >0:
-- print "Applying constraints"
-- self.con_nv = arr2nv(self.con)
-- flag = KINSetConstraints(self.solver, self.con_nv)
-- if flag < 0:
-- raise KINError(flag)
--
-- # Allocate internal memory
-- flag = KINInit(self.solver,kin_res,self.x_cur)
-- if flag < 0:
-- raise KINError(flag)
-- if self.print_level >0:
-- print "KINSOL initialized"
--
-- # Link to linear solver
-- if self.sparse:
-- flag = KINSLUG(self.solver,self.pData.dim)
-- else:
-- flag = KINPinv(self.solver,self.pData.dim)
-- #flag = KINDense(self.solver,self.pData.dim)
--
-- if flag < 0:
-- raise KINError(flag)
-- if self.print_level >0:
-- print "Linear solver connected"
--
-- # Set regularization parameter, if necessary
-- if self.reg_param != 0.0:
-- if self.sparse:
-- flag = KINSLUGSetRegParam(self.solver, self.reg_param)
-- else:
-- flag = KINPinvSetRegParam(self.solver, self.reg_param)
--
-- if flag < 0:
-- raise KINError(flag)
-- if self.print_level >0:
-- print "Reg param ", self.reg_param, " set"
-- else:
-- # If the user has specified constraints, connect them
-- if con != None:
-- if self.print_level >0:
-- print "Applying constraints"
-- self.con_nv = arr2nv(con)
-- flag = KINSetConstraints(self.solver, self.con_nv)
-- if flag < 0:
-- raise KINError(flag)
-- else:
-- flag = KINSetConstraints(self.solver, NULL)
-- if flag < 0:
-- raise KINError(flag)
--
-- # If the user supplied a Jacobien, link it to the solver
-- if self.pData.JAC != NULL:
-- if sparse:
-- flag = KINSLUGSetJacFn(self.solver,kin_sp_jac)
-- else:
-- flag = KINPinvSetJacFn(self.solver,kin_jac)
-- #flag = KINDlsSetDenseJacFn(self.solver,kin_jac)
--
-- if flag < 0:
-- raise KINError(flag)
-- if self.print_level >0:
-- print "Jacobian supplied by user connected"
-- else:
-- if sparse:
-- flag = KINSLUGSetJacFn(self.solver,NULL)
-- else:
-- flag = KINPinvSetJacFn(self.solver,NULL)
-- #flag = KINDlsSetDenseJacFn(self.solver,NULL)
--
-- if flag < 0:
-- raise KINError(flag)
--
-- # Link the solver to the supplied problem data
-- flag = KINSetUserData(self.solver,<void*>self.pData)
-- if flag < 0:
-- raise KINError(flag)
-- if self.print_level >0:
-- print "User data set"
--
-- # Create scaling vectors filled with ones
-- # since the problem is assumed to be scaled
-- self.x_scale = arr2nv(np.ones(self.pData.dim))
-- if self.fscale != None:
-- self.f_scale = arr2nv(self.fscale)
-- else:
-- self.f_scale = arr2nv(np.ones(self.pData.dim))
--
--
-- def KINSOL_solve(self,wo_LineSearch):
-- """
-- Function that should be called after a call to KINSOL_init
-- solves the function supplied as RHS
-- """
--
-- cdef:
-- int flag, DLSflag, lsflag, count,ind
-- long int nb_iters
-- ndarray x0
-- if self.print_level >0:
-- print "Calling solver..."
--
-- if not wo_LineSearch:
-- # Use LineSearch
-- flag = KINSol(<void*>self.solver,self.x_cur,KIN_LINESEARCH,self.x_scale,self.f_scale)
-- count = 0
-- if flag > 1:
-- # Probably stuck at a minimum
-- self.x_cur = self.x_init
-- flag = KINSol(<void*>self.solver,self.x_cur,KIN_NONE,self.x_scale,self.f_scale)
--
-- if flag > 1:
-- # Stuck at minimum again
-- raise KINError(flag)
-- else:
-- # Don't use linesearch
-- flag = -7
--
-- while flag <0:
-- # Get nbr of non-linear iterations
-- lsflag = KINGetNumNonlinSolvIters(<void*>self.solver, &nb_iters)
-- if lsflag != 0:
-- raise KINError(lsflag)
--
--
-- if (flag == -5 and nb_iters == 1):
-- # Cannot take a step bigger than zero on the first iteration
-- # Try with heuristic initial guess
-- raise KINError(42)
--
-- if (flag == -7 or flag == -6 or (flag == -5 and nb_iters > 1)):
-- if count == 0:
-- # First time
-- print "Calling solver again as simple newton."
-- self.x_cur = self.x_init
-- DLSflag = KINSetMaxSetupCalls(self.solver, 1);
-- if DLSflag < 0:
-- raise KINError(DLSflag)
--
-- elif count == 1:
-- # second time
-- print "Turning of residual monitoring"
-- DLSflag = KINSetNoResMon(self.solver, True);
-- if DLSflag < 0:
-- raise KINError(DLSflag)
--
-- elif count == 3:
-- # fourth time, release the bound on the step
--
-- print "Remove limit on stepsize"
-- DLSflag = KINSetMaxNewtonStep(self.solver, 1.0e34)
-- if DLSflag < 0:
-- raise KINError(DLSflag)
--
-- count += 1
-- if count >= 10:
-- if self.print_level >0:
-- print "Tried with simple newton ten times"
-- raise KINError(-7)
--
-- else:
--
-- print "Error in solve, flag: ", flag
-- lsflag = KINPinvGetLastFlag(self.solver, &lsflag)
-- #lsflag = KINDlsGetLastFlag(self.solver, &lsflag)
-- if lsflag != 0:
-- if lsflag <0:
-- print "Last flag from Linear solver: ", lsflag
-- else:
-- print "Jacobian singular at column ", lsflag
-- raise KINError(flag)
--
-- flag = KINSol(<void*>self.solver,self.x_cur,KIN_NONE,self.x_scale,self.f_scale)
-- if self.print_level >0:
-- print "Problem solved, returning result"
--
-- return nv2arr(self.x_cur)
-Index: src/lib/kinslug.h
-===================================================================
---- assimulo/lib/kinslug.h (revision 838)
-+++ assimulo/lib/kinslug.h (nonexistent)
-@@ -1,58 +0,0 @@
--/*
-- * Copyright (C) 2010 Modelon AB / Copyright (c) 2002, The Regents of the University of California.
-- *
-- * This program is free software: you can redistribute it and/or modify
-- * it under the terms of the GNU Lesser General Public License as published by
-- * the Free Software Foundation, version 3 of the License.
--
-- * This program is distributed in the hope that it will be useful,
-- * but WITHOUT ANY WARRANTY; without even the implied warranty of
-- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-- * GNU Lesser General Public License for more details.
--
-- * You should have received a copy of the GNU Lesser General Public License
-- * along with this program. If not, see <http://www.gnu.org/licenses/>.
-- *
-- * This file is a modification of the file kinsol_dense.h , revision 1.5,
-- * from the SUNDIALS suite. The file is modified by:
-- *
-- * Johan Ylikiiskilä - johan.ylikiiskila@gmail.com
-- *
-- */
--
--#ifdef __cplusplus /* wrapper to enable C++ usage */
--extern "C" {
--#endif
--
--#ifndef _KINSLU_H
--#define _KINSLU_H
--
--#include "kinsol_jmod_wSLU.h"
--#include <sundials/sundials_dense.h>
--
--/*
-- * -----------------------------------------------------------------
-- * Function : KINSLUG
-- * -----------------------------------------------------------------
-- * A call to the KINSLUG function links the main solver with the
-- * SLUG (SuperLU reGularization implementation) linear solver.
-- * Its arguments are as follows:
-- *
-- * kinmem - pointer to an internal memory block allocated during a
-- * prior call to KINCreate
-- *
-- * N - problem size
-- *
-- * The return value of KINSLUG is one of:
-- * 0 if successful
-- * int different from zero otherwise
-- * -----------------------------------------------------------------
-- */
--
-- SUNDIALS_EXPORT int KINSLUG(void *kinmem, int N);
--
--#endif
--
--#ifdef __cplusplus
--}
--#endif
-Index: src/lib/kinsol_jmod_wSLU.c
-===================================================================
---- assimulo/lib/kinsol_jmod_wSLU.c (revision 838)
-+++ assimulo/lib/kinsol_jmod_wSLU.c (nonexistent)
-@@ -1,530 +0,0 @@
--/*
-- * This file has been modified by Modelon AB, changes are Copyright
-- * (c) 2014 Modelon AB.
-- *
-- * Original copyright notice:
-- *
-- * Copyright (c) 2002, The Regents of the University of California.
-- * Produced at the Lawrence Livermore National Laboratory.
-- * Written by S.D. Cohen, A.C. Hindmarsh, R. Serban,
-- * D. Shumaker, and A.G. Taylor.
-- * UCRL-CODE-155951 (CVODE)
-- * UCRL-CODE-155950 (CVODES)
-- * UCRL-CODE-155952 (IDA)
-- * UCRL-CODE-237203 (IDAS)
-- * UCRL-CODE-155953 (KINSOL)
-- * All rights reserved.
--
-- * This file is part of SUNDIALS.
--
-- * Redistribution and use in source and binary forms, with or without
-- * modification, are permitted provided that the following conditions
-- * are met:
-- *
-- * 1. Redistributions of source code must retain the above copyright
-- * notice, this list of conditions and the disclaimer below.
-- *
-- * 2. Redistributions in binary form must reproduce the above copyright
-- * notice, this list of conditions and the disclaimer (as noted below)
-- * in the documentation and/or other materials provided with the
-- * distribution.
-- *
-- * 3. Neither the name of the UC/LLNL nor the names of its contributors
-- * may be used to endorse or promote products derived from this software
-- * without specific prior written permission.
-- *
-- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-- * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-- * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
-- * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
-- * REGENTS OF THE UNIVERSITY OF CALIFORNIA, THE U.S. DEPARTMENT OF ENERGY
-- * OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-- * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
-- * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
-- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
-- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-- *
-- * Additional BSD Notice
-- * ---------------------
-- * 1. This notice is required to be provided under our contract with
-- * the U.S. Department of Energy (DOE). This work was produced at the
-- * University of California, Lawrence Livermore National Laboratory
-- * under Contract No. W-7405-ENG-48 with the DOE.
-- *
-- * 2. Neither the United States Government nor the University of
-- * California nor any of their employees, makes any warranty, express
-- * or implied, or assumes any liability or responsibility for the
-- * accuracy, completeness, or usefulness of any information, apparatus,
-- * product, or process disclosed, or represents that its use would not
-- * infringe privately-owned rights.
-- *
-- * 3. Also, reference herein to any specific commercial products,
-- * process, or services by trade name, trademark, manufacturer or
-- * otherwise does not necessarily constitute or imply its endorsement,
-- * recommendation, or favoring by the United States Government or the
-- * University of California. The views and opinions of authors expressed
-- * herein do not necessarily state or reflect those of the United States
-- * Government or the University of California, and shall not be used for
-- * advertising or product endorsement purposes.
-- */
--
--/*
-- * =================================================================
-- * IMPORTED HEADER FILES
-- * =================================================================
-- */
--
--
--#include <stdio.h>
--#include <stdlib.h>
--
--#include "kinsol/kinsol_impl.h"
--#include "kinsol_jmod_impl_wSLU.h"
--#include <sundials/sundials_math.h>
--
--/*
-- * =================================================================
-- * FUNCTION SPECIFIC CONSTANTS
-- * =================================================================
-- */
--
--/* Constant for DQ Jacobian approximation */
--#define MIN_INC_MULT RCONST(1000.0)
--
--#define ZERO RCONST(0.0)
--#define ONE RCONST(1.0)
--#define TWO RCONST(2.0)
--
--/*
-- * =================================================================
-- * READIBILITY REPLACEMENTS
-- * =================================================================
-- */
--
--#define lrw1 (kin_mem->kin_lrw1)
--#define liw1 (kin_mem->kin_liw1)
--#define uround (kin_mem->kin_uround)
--#define func (kin_mem->kin_func)
--#define user_data (kin_mem->kin_user_data)
--#define printfl (kin_mem->kin_printfl)
--#define linit (kin_mem->kin_linit)
--#define lsetup (kin_mem->kin_lsetup)
--#define lsolve (kin_mem->kin_lsolve)
--#define lfree (kin_mem->kin_lfree)
--#define lmem (kin_mem->kin_lmem)
--#define inexact_ls (kin_mem->kin_inexact_ls)
--#define uu (kin_mem->kin_uu)
--#define fval (kin_mem->kin_fval)
--#define uscale (kin_mem->kin_uscale)
--#define fscale (kin_mem->kin_fscale)
--#define sqrt_relfunc (kin_mem->kin_sqrt_relfunc)
--#define sJpnorm (kin_mem->kin_sJpnorm)
--#define sfdotJp (kin_mem->kin_sfdotJp)
--#define errfp (kin_mem->kin_errfp)
--#define infofp (kin_mem->kin_infofp)
--#define setupNonNull (kin_mem->kin_setupNonNull)
--#define vtemp1 (kin_mem->kin_vtemp1)
--#define vec_tmpl (kin_mem->kin_vtemp1)
--#define vtemp2 (kin_mem->kin_vtemp2)
--
--#define mtype (kinpinv_mem->d_type)
--#define n (kinpinv_mem->d_n)
--#define ml (kinpinv_mem->d_ml)
--#define mu (kinpinv_mem->d_mu)
--#define smu (kinpinv_mem->d_smu)
--#define jacDQ (kinpinv_mem->d_jacDQ)
--#define djac (kinpinv_mem->d_djac)
--#define spjac (kinpinv_mem->d_spjac)
--#define bjac (kinpinv_mem->d_bjac)
--#define J (kinpinv_mem->d_J)
--#define pivots (kinpinv_mem->d_pivots)
--#define nje (kinpinv_mem->d_nje)
--#define nfeDQ (kinpinv_mem->d_nfeDQ)
--#define J_data (kinpinv_mem->d_J_data)
--#define last_flag (kinpinv_mem->d_last_flag)
--#define RTR (kinpinv_mem->d_RTR)
--#define reg_param (kinpinv_mem->d_reg_param)
--
--/*
-- * =================================================================
-- * EXPORTED FUNCTIONS
-- * =================================================================
-- */
--
--/*
-- * -----------------------------------------------------------------
-- * KINDlsSetJacFn
-- * -----------------------------------------------------------------
-- */
--
--int KINPinvSetJacFn(void *kinmem, KINPinvJacFn jac)
--{
-- KINMem kin_mem;
-- KINPinvMem kinpinv_mem;
--
-- /* Return immediately if kinmem is NULL */
-- if (kinmem == NULL) {
-- KINProcessError(NULL, KINPINV_MEM_NULL, "KINPINV", "KINPinvSetJacFn", MSGD_KINMEM_NULL);
-- return(KINPINV_MEM_NULL);
-- }
-- kin_mem = (KINMem) kinmem;
--
-- if (lmem == NULL) {
-- KINProcessError(kin_mem, KINPINV_LMEM_NULL, "KINPINV", "KINPinvSetJacFn", MSGD_LMEM_NULL);
-- return(KINPINV_LMEM_NULL);
-- }
-- kinpinv_mem = (KINPinvMem) lmem;
--
-- if (jac != NULL) {
-- jacDQ = FALSE;
-- djac = jac;
-- } else {
-- jacDQ = TRUE;
-- }
--
--
-- return(KINPINV_SUCCESS);
--}
--
--/*
-- * -----------------------------------------------------------------
-- * KINPinvSetRegParam
-- * -----------------------------------------------------------------
-- */
--
--int KINPinvSetRegParam(void *kinmem, realtype reg_p)
--{
-- KINMem kin_mem;
-- KINPinvMem kinpinv_mem;
--
-- /* Return immediately if kinmem is NULL */
-- if (kinmem == NULL) {
-- KINProcessError(NULL, KINPINV_MEM_NULL, "KINPINV", "KINPinvSetJacFn", MSGD_KINMEM_NULL);
-- return(KINPINV_MEM_NULL);
-- }
-- kin_mem = (KINMem) kinmem;
--
-- if (lmem == NULL) {
-- KINProcessError(kin_mem, KINPINV_LMEM_NULL, "KINPINV", "KINPinvSetJacFn", MSGD_LMEM_NULL);
-- return(KINPINV_LMEM_NULL);
-- }
-- kinpinv_mem = (KINPinvMem) lmem;
--
-- reg_param = reg_p;
--
-- return(KINPINV_SUCCESS);
--}
--
--/*
-- * -----------------------------------------------------------------
-- * KINSLUGSetRegParam
-- * -----------------------------------------------------------------
-- */
--
--int KINSLUGSetRegParam(void *kinmem, realtype reg_p)
--{
-- KINMem kin_mem;
-- KINPinvMem kinpinv_mem;
--
-- /* Return immediately if kinmem is NULL */
-- if (kinmem == NULL) {
-- KINProcessError(NULL, KINPINV_MEM_NULL, "KINPINV", "KINPinvSetJacFn", MSGD_KINMEM_NULL);
-- return(KINPINV_MEM_NULL);
-- }
-- kin_mem = (KINMem) kinmem;
--
-- if (lmem == NULL) {
-- KINProcessError(kin_mem, KINPINV_LMEM_NULL, "KINPINV", "KINPinvSetJacFn", MSGD_LMEM_NULL);
-- return(KINPINV_LMEM_NULL);
-- }
-- kinpinv_mem = (KINPinvMem) lmem;
--
-- reg_param = reg_p;
--
-- return(KINPINV_SUCCESS);
--}
--
--int KINSLUGSetJacFn(void *kinmem, KINSLUGJacFn jac)
--{
-- KINMem kin_mem;
-- KINPinvMem kinpinv_mem;
--
-- /* Return immediately if kinmem is NULL */
-- if (kinmem == NULL) {
-- KINProcessError(NULL, KINPINV_MEM_NULL, "KINSLUG", "KINSLUGSetJacFn", MSGD_KINMEM_NULL);
-- return(KINPINV_MEM_NULL);
-- }
-- kin_mem = (KINMem) kinmem;
--
-- if (lmem == NULL) {
-- KINProcessError(kin_mem, KINPINV_LMEM_NULL, "KINSLUG", "KINSLUGSetJacFn", MSGD_LMEM_NULL);
-- return(KINPINV_LMEM_NULL);
-- }
-- kinpinv_mem = (KINPinvMem) lmem;
--
-- if (jac != NULL) {
-- jacDQ = FALSE;
-- spjac = jac;
-- } else {
-- jacDQ = TRUE;
-- }
--
-- return(KINPINV_SUCCESS);
--}
--/*
-- * -----------------------------------------------------------------
-- * KINDlsGetWorkSpace
-- * -----------------------------------------------------------------
-- */
--
--int KINPinvGetWorkSpace(void *kinmem, long int *lenrwLS, long int *leniwLS)
--{
-- KINMem kin_mem;
-- KINPinvMem kinpinv_mem;
--
-- /* Return immediately if kinmem is NULL */
-- if (kinmem == NULL) {
-- KINProcessError(NULL, KINPINV_MEM_NULL, "KINPINV", "KINPinvGetWorkSpace", MSGD_KINMEM_NULL);
-- return(KINPINV_MEM_NULL);
-- }
-- kin_mem = (KINMem) kinmem;
--
-- if (lmem == NULL) {
-- KINProcessError(kin_mem, KINPINV_LMEM_NULL, "KINPINV", "KINPinvGetWorkSpace", MSGD_LMEM_NULL);
-- return(KINPINV_LMEM_NULL);
-- }
-- kinpinv_mem = (KINPinvMem) lmem;
--
-- if (mtype == SUNDIALS_DENSE) {
-- *lenrwLS = n*n;
-- *leniwLS = n;
-- } else if (mtype == SUNDIALS_BAND) {
-- *lenrwLS = n*(smu + mu + 2*ml + 2);
-- *leniwLS = n;
-- }
--
-- return(KINPINV_SUCCESS);
--}
--
--/*
-- * -----------------------------------------------------------------
-- * KINPinvGetNumJacEvals
-- * -----------------------------------------------------------------
-- */
--
--int KINPinvGetNumJacEvals(void *kinmem, long int *njevals)
--{
-- KINMem kin_mem;
-- KINPinvMem kinpinv_mem;
--
-- /* Return immediately if kinmem is NULL */
-- if (kinmem == NULL) {
-- KINProcessError(NULL, KINPINV_MEM_NULL, "KINPINV", "KINPinvGetNumJacEvals", MSGD_KINMEM_NULL);
-- return(KINPINV_MEM_NULL);
-- }
-- kin_mem = (KINMem) kinmem;
--
-- if (lmem == NULL) {
-- KINProcessError(kin_mem, KINPINV_LMEM_NULL, "KINPINV", "KINPinvGetNumJacEvals", MSGD_LMEM_NULL);
-- return(KINPINV_LMEM_NULL);
-- }
-- kinpinv_mem = (KINPinvMem) lmem;
--
-- *njevals = nje;
--
-- return(KINPINV_SUCCESS);
--}
--
--/*
-- * -----------------------------------------------------------------
-- * KINPinvGetNumFuncEvals
-- * -----------------------------------------------------------------
-- */
--
--int KINPinvGetNumFuncEvals(void *kinmem, long int *nfevalsLS)
--{
-- KINMem kin_mem;
-- KINPinvMem kinpinv_mem;
--
-- /* Return immediately if kinmem is NULL */
-- if (kinmem == NULL) {
-- KINProcessError(NULL, KINPINV_MEM_NULL, "KINPINV", "KINPinvGetNumFuncEvals", MSGD_KINMEM_NULL);
-- return(KINPINV_MEM_NULL);
-- }
-- kin_mem = (KINMem) kinmem;
--
-- if (lmem == NULL) {
-- KINProcessError(kin_mem, KINPINV_LMEM_NULL, "KINPINV", "KINPinvGetNumGuncEvals", MSGD_LMEM_NULL);
-- return(KINPINV_LMEM_NULL);
-- }
-- kinpinv_mem = (KINPinvMem) lmem;
--
-- *nfevalsLS = nfeDQ;
--
-- return(KINPINV_SUCCESS);
--}
--
--/*
-- * -----------------------------------------------------------------
-- * KINPinvGetLastFlag
-- * -----------------------------------------------------------------
-- */
--
--int KINPinvGetLastFlag(void *kinmem, int *flag)
--{
-- KINMem kin_mem;
-- KINPinvMem kinpinv_mem;
--
-- /* Return immediately if kinmem is NULL */
-- if (kinmem == NULL) {
-- KINProcessError(NULL, KINPINV_MEM_NULL, "KINPINV", "KINPinvGetLastFlag", MSGD_KINMEM_NULL);
-- return(KINPINV_MEM_NULL);
-- }
-- kin_mem = (KINMem) kinmem;
--
-- if (lmem == NULL) {
-- KINProcessError(kin_mem, KINPINV_LMEM_NULL, "KINPINV", "KINPinvGetLastFlag", MSGD_LMEM_NULL);
-- return(KINPINV_LMEM_NULL);
-- }
-- kinpinv_mem = (KINPinvMem) lmem;
--
-- *flag = last_flag;
--
-- return(KINPINV_SUCCESS);
--}
--
--/*
-- * -----------------------------------------------------------------
-- * KINPinvGetReturnFlagName
-- * -----------------------------------------------------------------
-- */
--
--char *KINPinvGetReturnFlagName(int flag)
--{
-- char *name;
--
-- name = (char *)malloc(30*sizeof(char));
--
-- switch(flag) {
-- case KINPINV_SUCCESS:
-- sprintf(name, "KINPINV_SUCCESS");
-- break;
-- case KINPINV_MEM_NULL:
-- sprintf(name, "KINPINV_MEM_NULL");
-- break;
-- case KINPINV_LMEM_NULL:
-- sprintf(name, "KINPINV_LMEM_NULL");
-- break;
-- case KINPINV_ILL_INPUT:
-- sprintf(name, "KINPINV_ILL_INPUT");
-- break;
-- case KINPINV_MEM_FAIL:
-- sprintf(name, "KINPINV_MEM_FAIL");
-- break;
-- default:
-- sprintf(name, "NONE");
-- }
--
-- return(name);
--}
--
--/*
-- * =================================================================
-- * DQ JACOBIAN APPROXIMATIONS
-- * =================================================================
-- */
--
--
--
--
--/*
-- * -----------------------------------------------------------------
-- * kinPinvDQJac
-- * -----------------------------------------------------------------
-- * This routine generates a dense difference quotient approximation to
-- * the Jacobian of F(u). It assumes that a dense matrix of type
-- * DlsMat is stored column-wise, and that elements within each column
-- * are contiguous. The address of the jth column of J is obtained via
-- * the macro DENSE_COL and this pointer is associated with an N_Vector
-- * using the N_VGetArrayPointer/N_VSetArrayPointer functions.
-- * Finally, the actual computation of the jth column of the Jacobian is
-- * done with a call to N_VLinearSum.
-- *
-- * The increment used in the finite-difference approximation
-- * J_ij = ( F_i(u+sigma_j * e_j) - F_i(u) ) / sigma_j
-- * is
-- * sigma_j = max{|u_j|, |1/uscale_j|} * sqrt(uround)
-- *
-- * Note: uscale_j = 1/typ(u_j)
-- *
-- * NOTE: Any type of failure of the system function her leads to an
-- * unrecoverable failure of the Jacobian function and thus
-- * of the linear solver setup function, stopping KINSOL.
-- * -----------------------------------------------------------------
-- */
--
--int kinPinvDQJac(int N,
-- N_Vector u, N_Vector fu,
-- DlsMat Jac, void *data,
-- N_Vector tmp1, N_Vector tmp2)
--{
-- realtype inc, inc_inv, ujsaved, ujscale, sign;
-- realtype *tmp2_data, *u_data, *uscale_data;
-- N_Vector ftemp, jthCol;
-- long int j;
-- int retval = 0;
--
-- KINMem kin_mem;
-- KINPinvMem kinpinv_mem;
--
-- /* data points to kin_mem */
-- kin_mem = (KINMem) data;
-- kinpinv_mem = (KINPinvMem) lmem;
--
-- /* Save pointer to the array in tmp2 */
-- tmp2_data = N_VGetArrayPointer(tmp2);
--
-- /* Rename work vectors for readibility */
-- ftemp = tmp1;
-- jthCol = tmp2;
--
-- /* Obtain pointers to the data for u and uscale */
-- u_data = N_VGetArrayPointer(u);
-- uscale_data = N_VGetArrayPointer(uscale);
--
-- /* This is the only for loop for 0..N-1 in KINSOL */
--
-- for (j = 0; j < N; j++) {
--
-- /* Generate the jth col of Jac(u) */
--
-- N_VSetArrayPointer(DENSE_COL(Jac,j), jthCol);
--
-- ujsaved = u_data[j];
-- ujscale = ONE/uscale_data[j];
-- sign = (ujsaved >= ZERO) ? ONE : -ONE;
--#if SUNDIALS_26
-- inc = sqrt_relfunc*SUNMAX(SUNRabs(ujsaved), ujscale)*sign;
--#else
-- inc = sqrt_relfunc*MAX(ABS(ujsaved), ujscale)*sign;
--#endif
-- u_data[j] += inc;
--
-- retval = func(u, ftemp, user_data);
-- nfeDQ++;
-- if (retval != 0) break;
--
-- u_data[j] = ujsaved;
--
-- inc_inv = ONE/inc;
-- N_VLinearSum(inc_inv, ftemp, -inc_inv, fu, jthCol);
--
-- }
--
-- /* Restore original array pointer in tmp2 */
-- N_VSetArrayPointer(tmp2_data, tmp2);
--
-- return(retval);
--}
-Index: src/lib/kinpinv.h
-===================================================================
---- assimulo/lib/kinpinv.h (revision 838)
-+++ assimulo/lib/kinpinv.h (nonexistent)
-@@ -1,107 +0,0 @@
--/*
-- * This file has been modified by Modelon AB, changes are Copyright
-- * (c) 2014 Modelon AB.
-- *
-- * Original copyright notice:
-- *
-- * Copyright (c) 2002, The Regents of the University of California.
-- * Produced at the Lawrence Livermore National Laboratory.
-- * Written by S.D. Cohen, A.C. Hindmarsh, R. Serban,
-- * D. Shumaker, and A.G. Taylor.
-- * UCRL-CODE-155951 (CVODE)
-- * UCRL-CODE-155950 (CVODES)
-- * UCRL-CODE-155952 (IDA)
-- * UCRL-CODE-237203 (IDAS)
-- * UCRL-CODE-155953 (KINSOL)
-- * All rights reserved.
--
-- * This file is part of SUNDIALS.
--
-- * Redistribution and use in source and binary forms, with or without
-- * modification, are permitted provided that the following conditions
-- * are met:
-- *
-- * 1. Redistributions of source code must retain the above copyright
-- * notice, this list of conditions and the disclaimer below.
-- *
-- * 2. Redistributions in binary form must reproduce the above copyright
-- * notice, this list of conditions and the disclaimer (as noted below)
-- * in the documentation and/or other materials provided with the
-- * distribution.
-- *
-- * 3. Neither the name of the UC/LLNL nor the names of its contributors
-- * may be used to endorse or promote products derived from this software
-- * without specific prior written permission.
-- *
-- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-- * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-- * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
-- * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
-- * REGENTS OF THE UNIVERSITY OF CALIFORNIA, THE U.S. DEPARTMENT OF ENERGY
-- * OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-- * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
-- * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
-- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
-- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-- *
-- * Additional BSD Notice
-- * ---------------------
-- * 1. This notice is required to be provided under our contract with
-- * the U.S. Department of Energy (DOE). This work was produced at the
-- * University of California, Lawrence Livermore National Laboratory
-- * under Contract No. W-7405-ENG-48 with the DOE.
-- *
-- * 2. Neither the United States Government nor the University of
-- * California nor any of their employees, makes any warranty, express
-- * or implied, or assumes any liability or responsibility for the
-- * accuracy, completeness, or usefulness of any information, apparatus,
-- * product, or process disclosed, or represents that its use would not
-- * infringe privately-owned rights.
-- *
-- * 3. Also, reference herein to any specific commercial products,
-- * process, or services by trade name, trademark, manufacturer or
-- * otherwise does not necessarily constitute or imply its endorsement,
-- * recommendation, or favoring by the United States Government or the
-- * University of California. The views and opinions of authors expressed
-- * herein do not necessarily state or reflect those of the United States
-- * Government or the University of California, and shall not be used for
-- * advertising or product endorsement purposes.
-- */
--
--#ifdef __cplusplus /* wrapper to enable C++ usage */
--extern "C" {
--#endif
--
--#ifndef _PINV_H
--#define _PINV_H
--
--#include "kinsol_jmod.h"
--#include <sundials/sundials_dense.h>
--
--/*
-- * -----------------------------------------------------------------
-- * Function : KINpinv
-- * -----------------------------------------------------------------
-- * A call to the KINpinv function links the main solver with the
-- * pinv linear solver. Its arguments are as follows:
-- *
-- * kinmem - pointer to an internal memory block allocated during a
-- * prior call to KINCreate
-- *
-- * N - problem size
-- *
-- * The return value of KINpinv is one of:
-- * 0 if successful
-- * int different from zero otherwise
-- * -----------------------------------------------------------------
-- */
--
-- SUNDIALS_EXPORT int KINPinv(void *kinmem, int N);
--
--#endif
--
--#ifdef __cplusplus
--}
--#endif
-Index: src/lib/kinsol_jmod_wSLU.h
-===================================================================
---- assimulo/lib/kinsol_jmod_wSLU.h (revision 838)
-+++ assimulo/lib/kinsol_jmod_wSLU.h (nonexistent)
-@@ -1,255 +0,0 @@
--/*
-- * This file has been modified by Modelon AB, changes are Copyright
-- * (c) 2014 Modelon AB.
-- *
-- * Original copyright notice:
-- *
-- * Copyright (c) 2002, The Regents of the University of California.
-- * Produced at the Lawrence Livermore National Laboratory.
-- * Written by S.D. Cohen, A.C. Hindmarsh, R. Serban,
-- * D. Shumaker, and A.G. Taylor.
-- * UCRL-CODE-155951 (CVODE)
-- * UCRL-CODE-155950 (CVODES)
-- * UCRL-CODE-155952 (IDA)
-- * UCRL-CODE-237203 (IDAS)
-- * UCRL-CODE-155953 (KINSOL)
-- * All rights reserved.
--
-- * This file is part of SUNDIALS.
--
-- * Redistribution and use in source and binary forms, with or without
-- * modification, are permitted provided that the following conditions
-- * are met:
-- *
-- * 1. Redistributions of source code must retain the above copyright
-- * notice, this list of conditions and the disclaimer below.
-- *
-- * 2. Redistributions in binary form must reproduce the above copyright
-- * notice, this list of conditions and the disclaimer (as noted below)
-- * in the documentation and/or other materials provided with the
-- * distribution.
-- *
-- * 3. Neither the name of the UC/LLNL nor the names of its contributors
-- * may be used to endorse or promote products derived from this software
-- * without specific prior written permission.
-- *
-- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-- * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-- * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
-- * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
-- * REGENTS OF THE UNIVERSITY OF CALIFORNIA, THE U.S. DEPARTMENT OF ENERGY
-- * OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-- * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
-- * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
-- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
-- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-- *
-- * Additional BSD Notice
-- * ---------------------
-- * 1. This notice is required to be provided under our contract with
-- * the U.S. Department of Energy (DOE). This work was produced at the
-- * University of California, Lawrence Livermore National Laboratory
-- * under Contract No. W-7405-ENG-48 with the DOE.
-- *
-- * 2. Neither the United States Government nor the University of
-- * California nor any of their employees, makes any warranty, express
-- * or implied, or assumes any liability or responsibility for the
-- * accuracy, completeness, or usefulness of any information, apparatus,
-- * product, or process disclosed, or represents that its use would not
-- * infringe privately-owned rights.
-- *
-- * 3. Also, reference herein to any specific commercial products,
-- * process, or services by trade name, trademark, manufacturer or
-- * otherwise does not necessarily constitute or imply its endorsement,
-- * recommendation, or favoring by the United States Government or the
-- * University of California. The views and opinions of authors expressed
-- * herein do not necessarily state or reflect those of the United States
-- * Government or the University of California, and shall not be used for
-- * advertising or product endorsement purposes.
-- */
--
--#ifndef _KINJMOD_H
--#define _KINJMOD_H
--
--#ifdef __cplusplus /* wrapper to enable C++ usage */
--extern "C" {
--#endif
--
--#include <sundials/sundials_direct.h>
--#include <sundials/sundials_nvector.h>
--
--#include "slu_ddefs.h"
--
--/*
-- * =================================================================
-- * K I N P I N V C O N S T A N T S
-- * =================================================================
-- */
--
--/*
-- * -----------------------------------------------------------------
-- * KINPINV return values
-- * -----------------------------------------------------------------
-- */
--
--#define KINPINV_SUCCESS 0
--#define KINPINV_MEM_NULL -1
--#define KINPINV_LMEM_NULL -2
--#define KINPINV_ILL_INPUT -3
--#define KINPINV_MEM_FAIL -4
--
--/* Additional last_flag values */
--
--#define KINPINV_JACFUNC_UNRECVR -5
--#define KINPINV_JACFUNC_RECVR -6
--
--/*
-- * =================================================================
-- * F U N C T I O N T Y P E S
-- * =================================================================
-- */
--
--/*
-- * -----------------------------------------------------------------
-- * Type: KINPinvJacFn
-- * -----------------------------------------------------------------
-- *
-- * A dense Jacobian approximation function Jac must be of type
-- * KINDlsDenseJacFn. Its parameters are:
-- *
-- * N - problem size.
-- *
-- * u - current iterate (unscaled) [input]
-- *
-- * fu - vector (type N_Vector) containing result of nonlinear
-- * system function evaluated at current iterate:
-- * fu = F(u) [input]
-- *
-- * J - dense matrix (of type DlsMat) that will be loaded
-- * by a KINDlsDenseJacFn with an approximation to the
-- * Jacobian matrix J = (dF_i/dy_j).
-- *
-- * user_data - pointer to user data - the same as the user_data
-- * parameter passed to KINSetFdata.
-- *
-- * tmp1, tmp2 - available scratch vectors (volatile storage)
-- *
-- * A KINPinvJacFn should return 0 if successful, a positive
-- * value if a recoverable error occurred, and a negative value if
-- * an unrecoverable error occurred.
-- *
-- * -----------------------------------------------------------------
-- *
-- * NOTE: The following are two efficient ways to load a dense Jac:
-- * (1) (with macros - no explicit data structure references)
-- * for (j=0; j < Neq; j++) {
-- * col_j = DENSE_COL(Jac,j);
-- * for (i=0; i < Neq; i++) {
-- * generate J_ij = the (i,j)th Jacobian element
-- * col_j[i] = J_ij;
-- * }
-- * }
-- * (2) (without macros - explicit data structure references)
-- * for (j=0; j < Neq; j++) {
-- * col_j = (Jac->data)[j];
-- * for (i=0; i < Neq; i++) {
-- * generate J_ij = the (i,j)th Jacobian element
-- * col_j[i] = J_ij;
-- * }
-- * }
-- * A third way, using the DENSE_ELEM(A,i,j) macro, is much less
-- * efficient in general. It is only appropriate for use in small
-- * problems in which efficiency of access is NOT a major concern.
-- *
-- * -----------------------------------------------------------------
-- * -----------------------------------------------------------------
-- * Type: KINSLUGJacFn
-- * -----------------------------------------------------------------
-- *
-- * Basically the same as above except that instead of passing a
-- * DlsMat to store the Jacobian SuperMatrix is passed.
-- *
-- * -----------------------------------------------------------------
-- */
--
--
--typedef int (*KINPinvJacFn)(int N,
-- N_Vector u, N_Vector fu,
-- DlsMat J, void *user_data,
-- N_Vector tmp1, N_Vector tmp2);
--
--typedef int (*KINSLUGJacFn)(int N,
-- N_Vector u, N_Vector fu,
-- SuperMatrix *J, void *user_data,
-- N_Vector tmp1, N_Vector tmp2);
--
--
--/*
-- * =================================================================
-- * E X P O R T E D F U N C T I O N S
-- * =================================================================
-- */
--
--/*
-- * -----------------------------------------------------------------
-- * Optional inputs to the KINPinv linear solver
-- * -----------------------------------------------------------------
-- *
-- * KINDlsSetDenseJacFn specifies the dense Jacobian approximation
-- * routine to be used for a direct dense linear solver.
-- *
-- * By default, a difference quotient approximation, supplied with
-- * the solver is used.
-- *
-- * The return value is one of:
-- * KINPINV_SUCCESS if successful
-- * KINPINV_MEM_NULL if the KINSOL memory was NULL
-- * KINPINV_LMEM_NULL if the linear solver memory was NULL
-- * -----------------------------------------------------------------
-- */
--
--SUNDIALS_EXPORT int KINPinvSetJacFn(void *kinmem, KINPinvJacFn jac);
--
--SUNDIALS_EXPORT int KINSLUGSetJacFn(void *kinmem, KINSLUGJacFn jac);
--
--SUNDIALS_EXPORT int KINSLUGSetRegParam(void *kinmem, realtype reg_p);
--
--SUNDIALS_EXPORT int KINPinvSetRegParam(void *kinmem, realtype reg_p);
--
--/*
-- * -----------------------------------------------------------------
-- * Optional outputs from a KINDLS linear solver
-- * -----------------------------------------------------------------
-- *
-- * KINPinvGetWorkSpace returns the real and integer workspace used
-- * by the KINDLS linear solver.
-- * KINPinvGetNumJacEvals returns the number of calls made to the
-- * Jacobian evaluation routine.
-- * KINPinvGetNumFuncEvals returns the number of calls to the user's F
-- * routine due to finite difference Jacobian
-- * evaluation.
-- * KINPinvGetLastFlag returns the last error flag set by any of
-- * the KINDLS interface functions.
-- * KINPinvGetReturnFlagName returns the name of the constant
-- * associated with a KINDLS return flag
-- *
-- * The return value of KINPinvGet* is one of:
-- * KINPINV_SUCCESS if successful
-- * KINPINV_MEM_NULL if the KINSOL memory was NULL
-- * KINPINV_LMEM_NULL if the linear solver memory was NULL
-- * -----------------------------------------------------------------
-- */
--
--SUNDIALS_EXPORT int KINPinvGetWorkSpace(void *kinmem, long int *lenrwB, long int *leniwB);
--SUNDIALS_EXPORT int KINPinvGetNumJacEvals(void *kinmem, long int *njevalsB);
--SUNDIALS_EXPORT int KINPinvGetNumFuncEvals(void *kinmem, long int *nfevalsB);
--SUNDIALS_EXPORT int KINPinvGetLastFlag(void *kinmem, int *flag);
--SUNDIALS_EXPORT char *KINPinvGetReturnFlagName(int flag);
--
--#ifdef __cplusplus
--}
--#endif
--
--#endif
-Index: src/lib/sundials_kinsol_core.pxi
-===================================================================
---- assimulo/lib/sundials_kinsol_core.pxi (revision 838)
-+++ assimulo/lib/sundials_kinsol_core.pxi (nonexistent)
-@@ -1,144 +0,0 @@
--#!/usr/bin/env python
--# -*- coding: utf-8 -*-
--
--# Copyright (C) 2010 Modelon AB
--#
--# This program is free software: you can redistribute it and/or modify
--# it under the terms of the GNU Lesser General Public License as published by
--# the Free Software Foundation, version 3 of the License.
--#
--# This program is distributed in the hope that it will be useful,
--# but WITHOUT ANY WARRANTY; without even the implied warranty of
--# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
--# GNU Lesser General Public License for more details.
--#
--# You should have received a copy of the GNU Lesser General Public License
--# along with this program. If not, see <http://www.gnu.org/licenses/>.
--
--"""
--Cython Wrapper for interfacing Python with KINSOL (Sundials Version 2.4.0)
--Johan Ylikiiskilä Modelon AB
--
--see also Jon Olav Vik:
--http://codespeak.net/pipermail/cython-dev/2009-June/005947.html
--
--This file contains the constants from Sundials.
--"""
--# -----------------------------------------------------------------
--# KINSOL return flags
--# -----------------------------------------------------------------
--
--DEF KIN_SUCCESS =0
--DEF KIN_INITIAL_GUESS_OK =1
--DEF KIN_STEP_LT_STPTOL =2
--
--DEF KIN_REC_ERR =5
--
--DEF KIN_WARNING =99
--
--DEF KIN_MEM_NULL =-1
--DEF KIN_ILL_INPUT =-2
--DEF KIN_NO_MALLOC =-3
--DEF KIN_MEM_FAIL =-4
--DEF KIN_LINESEARCH_NONCONV =-5
--DEF KIN_MAXITER_REACHED =-6
--DEF KIN_MXNEWT_5X_EXCEEDED =-7
--DEF KIN_LINESEARCH_BCFAIL =-8
--DEF KIN_LINSOLV_NO_RECOVERY =-9
--DEF KIN_LINIT_FAIL =-10
--DEF KIN_LSETUP_FAIL =-11
--DEF KIN_LSOLVE_FAIL =-12
--
--DEF KIN_SYSFUNC_FAIL =-13
--DEF KIN_FIRST_SYSFUNC_ERR =-14
--DEF KIN_REPTD_SYSFUNC_ERR =-15
--
--
--# -----------------------------------------------------------------
--# Enumeration for inputs to KINSetEtaForm (eta choice)
--# -----------------------------------------------------------------
--# KIN_ETACONSTANT : use constant value for eta (default value is
--# 0.1 but a different value can be specified via
--# a call to KINSetEtaConstValue)
--#
--# KIN_ETACHOICE1 : use choice #1 as given in Eisenstat and Walker's
--# paper of SIAM J.Sci.Comput.,17 (1996), pp 16-32,
--# wherein eta is defined to be:
--#
--# eta(k+1) = ABS(||F(u_k+1)||_L2-||F(u_k)+J(u_k)*p_k||_L2)
--# ---------------------------------------------
--# ||F(u_k)||_L2
--#
--# 1+sqrt(5)
--# eta_safe = eta(k)^ealpha where ealpha = ---------
--# 2
--#
--# KIN_ETACHOICE2 : use choice #2 as given in Eisenstat and Walker's
--# paper wherein eta is defined to be:
--#
--# [ ||F(u_k+1)||_L2 ]^ealpha
--# eta(k+1) = egamma * [ --------------- ]
--# [ ||F(u_k)||_L2 ]
--#
--# where egamma = [0,1] and ealpha = (1,2]
--#
--# eta_safe = egamma*(eta(k)^ealpha)
--#
--# Note: The default values of the scalar
--# coefficients egamma and ealpha (both required)
--# are egamma = 0.9 and ealpha = 2.0, but the
--# routine KINSetEtaParams can be used to specify
--# different values.
--#
--# When using either KIN_ETACHOICE1 or KIN_ETACHOICE2, if
--# eta_safe > 0.1 then the following safeguard is applied:
--#
--# eta(k+1) = MAX {eta(k+1), eta_safe}
--#
--# The following safeguards are always applied when using either
--# KIN_ETACHOICE1 or KIN_ETACHOICE2 so that eta_min <= eta <= eta_max:
--#
--# eta(k+1) = MAX {eta(k+1), eta_min}
--# eta(k+1) = MIN {eta(k+1), eta_max}
--#
--# where eta_min = 1.0e-4 and eta_max = 0.9 (see KINForcingTerm).
--# -----------------------------------------------------------------
--
--DEF KIN_ETACHOICE1 =1
--DEF KIN_ETACHOICE2 =2
--DEF KIN_ETACONSTANT =3
--
--# -----------------------------------------------------------------
--# Enumeration for global strategy
--# -----------------------------------------------------------------
--# Choices are KIN_NONE and KIN_LINESEARCH.
--# -----------------------------------------------------------------
--
--DEF KIN_NONE =0
--DEF KIN_LINESEARCH =1
--
--# -----------------------------------------------------------------
--# KINDirect constants
--# -----------------------------------------------------------------
--
--DEF KINDLS_SUCCESS =0
--
--DEF KINDLS_MEM_NULL =-1
--DEF KINDLS_LMEM_NULL =-2
--DEF KINDLS_ILL_INPUT =-3
--DEF KINDLS_MEM_FAIL =-4
--DEF KINDLS_JACFUNC_UNRECVR =-5
--DEF KINDLS_JACFUNC_RECVR =-6
--
--
--# -----------------------------------------------------------------
--# KINPinv constants
--# -----------------------------------------------------------------
--
--DEF KINPINV_SUCCESS = 0
--DEF KINPINV_MEM_NULL = -1
--DEF KINPINV_LMEM_NULL = -2
--DEF KINPINV_ILL_INPUT = -3
--DEF KINPINV_MEM_FAIL = -4
--DEF KINPINV_JACFUNC_UNRECVR = -5
--DEF KINPINV_JACFUNC_RECVR = -6
-Index: src/lib/kinsol_jmod_impl.h
-===================================================================
---- assimulo/lib/kinsol_jmod_impl.h (revision 838)
-+++ assimulo/lib/kinsol_jmod_impl.h (nonexistent)
-@@ -1,153 +0,0 @@
--/*
-- * This file has been modified by Modelon AB, changes are Copyright
-- * (c) 2014 Modelon AB.
-- *
-- * Original copyright notice:
-- *
-- * Copyright (c) 2002, The Regents of the University of California.
-- * Produced at the Lawrence Livermore National Laboratory.
-- * Written by S.D. Cohen, A.C. Hindmarsh, R. Serban,
-- * D. Shumaker, and A.G. Taylor.
-- * UCRL-CODE-155951 (CVODE)
-- * UCRL-CODE-155950 (CVODES)
-- * UCRL-CODE-155952 (IDA)
-- * UCRL-CODE-237203 (IDAS)
-- * UCRL-CODE-155953 (KINSOL)
-- * All rights reserved.
--
-- * This file is part of SUNDIALS.
--
-- * Redistribution and use in source and binary forms, with or without
-- * modification, are permitted provided that the following conditions
-- * are met:
-- *
-- * 1. Redistributions of source code must retain the above copyright
-- * notice, this list of conditions and the disclaimer below.
-- *
-- * 2. Redistributions in binary form must reproduce the above copyright
-- * notice, this list of conditions and the disclaimer (as noted below)
-- * in the documentation and/or other materials provided with the
-- * distribution.
-- *
-- * 3. Neither the name of the UC/LLNL nor the names of its contributors
-- * may be used to endorse or promote products derived from this software
-- * without specific prior written permission.
-- *
-- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-- * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-- * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
-- * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
-- * REGENTS OF THE UNIVERSITY OF CALIFORNIA, THE U.S. DEPARTMENT OF ENERGY
-- * OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-- * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
-- * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
-- * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
-- * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-- * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-- *
-- * Additional BSD Notice
-- * ---------------------
-- * 1. This notice is required to be provided under our contract with
-- * the U.S. Department of Energy (DOE). This work was produced at the
-- * University of California, Lawrence Livermore National Laboratory
-- * under Contract No. W-7405-ENG-48 with the DOE.
-- *
-- * 2. Neither the United States Government nor the University of
-- * California nor any of their employees, makes any warranty, express
-- * or implied, or assumes any liability or responsibility for the
-- * accuracy, completeness, or usefulness of any information, apparatus,
-- * product, or process disclosed, or represents that its use would not
-- * infringe privately-owned rights.
-- *
-- * 3. Also, reference herein to any specific commercial products,
-- * process, or services by trade name, trademark, manufacturer or
-- * otherwise does not necessarily constitute or imply its endorsement,
-- * recommendation, or favoring by the United States Government or the
-- * University of California. The views and opinions of authors expressed
-- * herein do not necessarily state or reflect those of the United States
-- * Government or the University of California, and shall not be used for
-- * advertising or product endorsement purposes.
-- */
--
--#ifndef _KINJMOD_IMPL_H
--#define _KINJMOD_IMPL_H
--
--#ifdef __cplusplus /* wrapper to enable C++ usage */
--extern "C" {
--#endif
--
--#include "kinsol_jmod.h"
--
--
--/*
-- * -----------------------------------------------------------------
-- * Types: KINPinvMemRec, KINPinvMem
-- * -----------------------------------------------------------------
-- * The type KINPinvMem is pointer to a KINPinvMemRec.
-- * This structure contains KINPinv solver-specific data.
-- * -----------------------------------------------------------------
-- */
--
--typedef struct KINPinvMemRec {
--
-- int d_type; /* SUNDIALS_DENSE or SUNDIALS_BAND */
--
-- int d_n; /* problem dimension */
--
-- int d_ml; /* lower bandwidth of Jacobian */
-- int d_mu; /* upper bandwidth of Jacobian */
-- int d_smu; /* upper bandwith of M = MIN(N-1,d_mu+d_ml) */
--
-- booleantype d_jacDQ; /* TRUE if using internal DQ Jacobian approx. */
-- KINPinvJacFn d_djac; /* dense Jacobian routine to be called */
--
-- void *d_J_data; /* J_data is passed to djac or bjac */
--
-- DlsMat d_J; /* problem Jacobian */
--
-- int *d_pivots; /* pivot array for PM = LU */
-- realtype *d_beta;
-- realtype d_reg_param; /* Regularization parameter */
-- long int d_nje; /* no. of calls to jac */
--
-- long int d_nfeDQ; /* no. of calls to F due to DQ Jacobian approx. */
--
-- int d_last_flag; /* last error return flag */
--
-- DlsMat d_JTJ;
-- booleantype d_regularized; /* Flag if the regularized problem should be solved*/
-- booleantype d_redojac;
--
--} *KINPinvMem;
--
--
--/*
-- * -----------------------------------------------------------------
-- * Prototypes of internal functions
-- * -----------------------------------------------------------------
-- */
--
--int kinPinvDQJac(int N,
-- N_Vector u, N_Vector fu,
-- DlsMat Jac, void *data,
-- N_Vector tmp1, N_Vector tmp2);
--
--/*
-- * -----------------------------------------------------------------
-- * Error Messages
-- * -----------------------------------------------------------------
-- */
--
--#define MSGD_KINMEM_NULL "KINSOL memory is NULL."
--#define MSGD_BAD_NVECTOR "A required vector operation is not implemented."
--#define MSGD_MEM_FAIL "A memory request failed."
--#define MSGD_LMEM_NULL "Linear solver memory is NULL."
--#define MSGD_BAD_SIZES "Illegal bandwidth parameter(s). Must have 0 <= ml, mu <= N-1."
--#define MSGD_JACFUNC_FAILED "The Jacobian routine failed in an unrecoverable manner."
--
--#ifdef __cplusplus
--}
--#endif
--
--#endif
diff --git a/r840.patch b/r840.patch
deleted file mode 100644
index eede3cd2b80e..000000000000
--- a/r840.patch
+++ /dev/null
@@ -1,206 +0,0 @@
-Index: setup.py
-===================================================================
---- setup.py (revision 839)
-+++ setup.py (revision 840)
-@@ -24,10 +24,17 @@
- import argparse
- import numpy.distutils as nd
- from distutils.version import StrictVersion
-+from os import listdir
-+from os.path import isfile, join
-
- def str2bool(v):
-- return v.lower() in ("yes", "true", "t", "1")
-+ return v.lower() in ("yes", "true", "t", "1")
-
-+def remove_prefix(name, prefix):
-+ if name.startswith(prefix):
-+ return name[len(prefix):]
-+ return name
-+
- parser = argparse.ArgumentParser(description='Assimulo setup script.')
- parser.register('type','bool',str2bool)
- package_arguments=['plugins','sundials','blas','superlu','lapack']
-@@ -36,7 +43,8 @@
- parser.add_argument("--{}-home".format(pg),
- help="Location of the {} directory".format(pg.upper()),type=str,default='')
- parser.add_argument("--blas-name", help="name of the blas package",default='blas')
--parser.add_argument("--extra-c-flags", help='Extra C-flags (a list enclosed in " ")',default='')
-+parser.add_argument("--extra-c-flags", help='Extra C-flags (a list enclosed in " ")',default='')
-+parser.add_argument("--with_openmp", type='bool', help="set to true if present",default=False)
- parser.add_argument("--is_static", type='bool', help="set to true if present",default=False)
- parser.add_argument("--sundials-with-superlu", type='bool', help="set to true if Sundials has been compiled with SuperLU",default=False)
- parser.add_argument("--debug", type='bool', help="set to true if present",default=False)
-@@ -120,7 +128,6 @@
- self.static = args[0].is_static
- self.static_link_gcc = ["-static-libgcc"] if self.static else []
- self.static_link_gfortran = ["-static-libgfortran"] if self.static else []
-- self.debug_flag = args[0].debug
- self.force_32bit = args[0].force_32bit
- self.flag_32bit = ["-m32"] if self.force_32bit else []
- self.no_mvscr = args[0].no_msvcr
-@@ -128,7 +135,7 @@
- self.extra_fortran_link_flags = args[0].extra_fortran_link_flags.split()
- self.extra_fortran_link_files = args[0].extra_fortran_link_files.split()
- self.thirdparty_methods = thirdparty_methods
--
-+ self.with_openmp = args[0].with_openmp
-
-
- if self.no_mvscr:
-@@ -270,12 +277,10 @@
- """
- Check if SuperLU package installed
- """
-- self.with_SLU = self.with_BLAS
-- kinsol_msg='KINSOL will not be compiled with support for SuperLU'
-+ self.with_SLU = True
-+ sundials_msg='SUNDIALS will not be compiled with support for SuperLU'
-
-- if not self.with_BLAS:
-- L.warning(kinsol_msg+' as BLAS is missing.')
-- elif self.SLUdir != "":
-+ if self.SLUdir != "":
- self.SLUincdir = os.path.join(self.SLUdir,'SRC')
- self.SLUlibdir = os.path.join(self.SLUdir,'lib')
- if not os.path.exists(os.path.join(self.SLUincdir,'supermatrix.h')):
-@@ -283,16 +288,18 @@
- L.warning("Could not find SuperLU, disabling support. View more information using --log=DEBUG")
- L.debug("Could not find SuperLU at the given path {}.".format(self.SLUdir))
- L.debug("usage: --superlu-home path")
-- L.debug(kinsol_msg+'.')
-+ L.debug(sundials_msg+'.')
- else:
- L.debug("SuperLU found in {} and {}: ".format(self.SLUincdir, self.SLUlibdir))
-+
-+ self.superLUFiles = [remove_prefix(f.split(".")[0],"lib") for f in listdir(self.SLUlibdir) if isfile(join(self.SLUlibdir, f)) and f.endswith(".a")]
-+ self.superLUFiles.sort(reverse=True)
- else:
- L.warning("No path to SuperLU supplied, disabling support. View more information using --log=DEBUG")
-- L.debug("No path to SuperLU supplied, KINSOL will not be compiled with support for SUperLU.")
-+ L.debug("No path to SuperLU supplied, SUNDIALS will not be compiled with support for SuperLU.")
- L.debug("usage: --superlu-home=path")
- L.debug("Note: the path required is to the folder where the folders 'SRC' and 'lib' are found.")
- self.with_SLU = False
-- L.debug(kinsol_msg+'.')
-
- def check_SUNDIALS(self):
- """
-@@ -301,13 +308,22 @@
- if os.path.exists(os.path.join(os.path.join(self.incdirs,'cvodes'), 'cvodes.h')):
- self.with_SUNDIALS=True
- L.debug('SUNDIALS found.')
-+ sundials_version = None
-
-- if os.path.exists(os.path.join(os.path.join(self.incdirs,'arkode'), 'arkode.h')): #This was added in 2.6
-- sundials_version = (2,6,0)
-- L.debug('SUNDIALS 2.6 found.')
-- else:
-- sundials_version = (2,5,0)
-- L.debug('SUNDIALS 2.5 found.')
-+ try:
-+ if os.path.exists(os.path.join(os.path.join(self.incdirs,'sundials'), 'sundials_config.h')):
-+ with open(os.path.join(os.path.join(self.incdirs,'sundials'), 'sundials_config.h')) as f:
-+ for line in f:
-+ if "SUNDIALS_PACKAGE_VERSION" in line:
-+ sundials_version = tuple([int(f) for f in line.split()[-1][1:-1].split(".")])
-+ L.debug('SUNDIALS %d.%d found.'%(sundials_version[0], sundials_version[1]))
-+ except Exception:
-+ if os.path.exists(os.path.join(os.path.join(self.incdirs,'arkode'), 'arkode.h')): #This was added in 2.6
-+ sundials_version = (2,6,0)
-+ L.debug('SUNDIALS 2.6 found.')
-+ else:
-+ sundials_version = (2,5,0)
-+ L.debug('SUNDIALS 2.5 found.')
-
- self.SUNDIALS_version = sundials_version
-
-@@ -367,6 +383,10 @@
- ext_list[-1].include_dirs = [np.get_include(), "assimulo","assimulo"+os.sep+"lib", self.incdirs]
- ext_list[-1].library_dirs = [self.libdirs]
- ext_list[-1].libraries = ["sundials_cvodes", "sundials_nvecserial", "sundials_idas"]
-+ if self.with_SLU: #If SUNDIALS is compiled with support for SuperLU
-+ ext_list[-1].include_dirs.append(self.SLUincdir)
-+ ext_list[-1].library_dirs.append(self.SLUlibdir)
-+ ext_list[-1].libraries.extend(self.superLUFiles)
-
- #Kinsol
- ext_list += cythonize(["assimulo"+os.path.sep+"solvers"+os.path.sep+"kinsol.pyx"],
-@@ -386,6 +406,9 @@
- el.extra_compile_args = ["-O2", "-fno-strict-aliasing"]
- if self.platform == "mac":
- el.extra_compile_args += ["-Wno-error=return-type"]
-+ if self.with_openmp:
-+ el.extra_link_args.append("-fopenmp")
-+ el.extra_compile_args.append("-fopenmp")
- el.extra_compile_args += self.flag_32bit + self.extra_c_flags
-
- for el in ext_list:
-Index: src/lib/sundials_callbacks.pxi
-===================================================================
---- assimulo/lib/sundials_callbacks.pxi (revision 839)
-+++ assimulo/lib/sundials_callbacks.pxi (revision 840)
-@@ -110,9 +110,14 @@
- cdef int ret_nnz
- cdef int dim = Jacobian.N
- cdef realtype* data = Jacobian.data
-- cdef int* rowvals = Jacobian.rowvals
-- cdef int* colptrs = Jacobian.colptrs
-
-+ IF SUNDIALS_VERSION >= (2,6,3):
-+ cdef int* rowvals = Jacobian.rowvals[0]
-+ cdef int* colptrs = Jacobian.colptrs[0]
-+ ELSE:
-+ cdef int* rowvals = Jacobian.rowvals
-+ cdef int* colptrs = Jacobian.colptrs
-+
- nv2arr_inplace(yv, y)
- """
- realtype *data;
-@@ -136,9 +141,9 @@
- jac = sparse.csc.csc_matrix(jac)
- raise AssimuloException("The Jacobian must be stored on Scipy's CSC format.")
- ret_nnz = jac.nnz
-- if ret_nnz> nnz:
-+ if ret_nnz > nnz:
- raise AssimuloException("The Jacobian has more entries than supplied to the problem class via 'jac_nnz'")
--
-+
- for i in range(min(ret_nnz,nnz)):
- data[i] = jac.data[i]
- rowvals[i] = jac.indices[i]
-Index: src/lib/sundials_includes.pxd
-===================================================================
---- assimulo/lib/sundials_includes.pxd (revision 839)
-+++ assimulo/lib/sundials_includes.pxd (revision 840)
-@@ -93,13 +93,29 @@
- ctypedef _DlsMat* DlsMat
- cdef realtype* DENSE_COL(DlsMat A, int j)
-
--IF SUNDIALS_VERSION >= (2,6,0):
-+IF SUNDIALS_VERSION >= (2,6,3):
- cdef extern from "sundials/sundials_sparse.h":
- cdef struct _SlsMat:
- int M
- int N
- int NNZ
-+ int NP
- realtype *data
-+ int sparsetype
-+ int *indexvals
-+ int *indexptrs
-+ int **rowvals
-+ int **colptrs
-+ int **colvals
-+ int **rowptrs
-+ ctypedef _SlsMat* SlsMat
-+ELIF SUNDIALS_VERSION >= (2,6,0):
-+ cdef extern from "sundials/sundials_sparse.h":
-+ cdef struct _SlsMat:
-+ int M
-+ int N
-+ int NNZ
-+ realtype *data
- int *rowvals
- int *colptrs
- ctypedef _SlsMat* SlsMat
diff --git a/r841.patch b/r841.patch
deleted file mode 100644
index d7aa11b3ea6f..000000000000
--- a/r841.patch
+++ /dev/null
@@ -1,13 +0,0 @@
-Index: setup.py
-===================================================================
---- setup.py (revision 840)
-+++ setup.py (revision 841)
-@@ -383,7 +383,7 @@
- ext_list[-1].include_dirs = [np.get_include(), "assimulo","assimulo"+os.sep+"lib", self.incdirs]
- ext_list[-1].library_dirs = [self.libdirs]
- ext_list[-1].libraries = ["sundials_cvodes", "sundials_nvecserial", "sundials_idas"]
-- if self.with_SLU: #If SUNDIALS is compiled with support for SuperLU
-+ if self.sundials_with_superlu and self.with_SLU: #If SUNDIALS is compiled with support for SuperLU
- ext_list[-1].include_dirs.append(self.SLUincdir)
- ext_list[-1].library_dirs.append(self.SLUlibdir)
- ext_list[-1].libraries.extend(self.superLUFiles)
diff --git a/r845.patch b/r845.patch
deleted file mode 100644
index dec23fc92e53..000000000000
--- a/r845.patch
+++ /dev/null
@@ -1,1689 +0,0 @@
-Index: setup.py
-===================================================================
---- setup.py (revision 844)
-+++ setup.py (revision 845)
-@@ -309,15 +309,30 @@
- self.with_SUNDIALS=True
- L.debug('SUNDIALS found.')
- sundials_version = None
-+ sundials_vector_type_size = None
-
- try:
- if os.path.exists(os.path.join(os.path.join(self.incdirs,'sundials'), 'sundials_config.h')):
- with open(os.path.join(os.path.join(self.incdirs,'sundials'), 'sundials_config.h')) as f:
- for line in f:
-- if "SUNDIALS_PACKAGE_VERSION" in line:
-+ if "SUNDIALS_PACKAGE_VERSION" in line or "SUNDIALS_VERSION" in line:
- sundials_version = tuple([int(f) for f in line.split()[-1][1:-1].split(".")])
- L.debug('SUNDIALS %d.%d found.'%(sundials_version[0], sundials_version[1]))
-- except Exception:
-+ break
-+ with open(os.path.join(os.path.join(self.incdirs,'sundials'), 'sundials_config.h')) as f:
-+ for line in f:
-+ if "SUNDIALS_INT32_T" in line and line.startswith("#define"):
-+ sundials_vector_type_size = "32"
-+ L.debug('SUNDIALS vector type size %s bit found.'%(sundials_vector_type_size))
-+ break
-+ if "SUNDIALS_INT64_T" in line and line.startswith("#define"):
-+ sundials_vector_type_size = "64"
-+ L.debug('SUNDIALS vector type size %s bit found.'%(sundials_vector_type_size))
-+ if self.with_SLU:
-+ L.warning("It is recommended to set the SUNDIALS_INDEX_TYPE to an 32bit integer when using SUNDIALS together with SuperLU.")
-+ L.warning("SuperLU may not function properly.")
-+ break
-+ except Exception as e:
- if os.path.exists(os.path.join(os.path.join(self.incdirs,'arkode'), 'arkode.h')): #This was added in 2.6
- sundials_version = (2,6,0)
- L.debug('SUNDIALS 2.6 found.')
-@@ -326,6 +341,7 @@
- L.debug('SUNDIALS 2.5 found.')
-
- self.SUNDIALS_version = sundials_version
-+ self.SUNDIALS_vector_size = sundials_vector_type_size
-
- else:
- L.warning(("Could not find Sundials, check the provided path (--sundials-home={}) "+
-@@ -375,7 +391,8 @@
- # SUNDIALS
- if self.with_SUNDIALS:
- compile_time_env = {'SUNDIALS_VERSION': self.SUNDIALS_version,
-- 'SUNDIALS_WITH_SUPERLU': self.sundials_with_superlu}
-+ 'SUNDIALS_WITH_SUPERLU': self.sundials_with_superlu,
-+ 'SUNDIALS_VECTOR_SIZE': self.SUNDIALS_vector_size}
- #CVode and IDA
- ext_list += cythonize(["assimulo" + os.path.sep + "solvers" + os.path.sep + "sundials.pyx"],
- include_path=[".","assimulo","assimulo" + os.sep + "lib"],
-@@ -382,11 +399,19 @@
- compile_time_env=compile_time_env, force=True)
- ext_list[-1].include_dirs = [np.get_include(), "assimulo","assimulo"+os.sep+"lib", self.incdirs]
- ext_list[-1].library_dirs = [self.libdirs]
-- ext_list[-1].libraries = ["sundials_cvodes", "sundials_nvecserial", "sundials_idas"]
-+
-+ if self.SUNDIALS_version >= (3,0,0):
-+ ext_list[-1].libraries = ["sundials_cvodes", "sundials_nvecserial", "sundials_idas", "sundials_sunlinsoldense", "sundials_sunlinsolspgmr", "sundials_sunmatrixdense", "sundials_sunmatrixsparse"]
-+ else:
-+ ext_list[-1].libraries = ["sundials_cvodes", "sundials_nvecserial", "sundials_idas"]
- if self.sundials_with_superlu and self.with_SLU: #If SUNDIALS is compiled with support for SuperLU
-+ if self.SUNDIALS_version >= (3,0,0):
-+ ext_list[-1].libraries.extend(["sundials_sunlinsolsuperlumt"])
-+
- ext_list[-1].include_dirs.append(self.SLUincdir)
- ext_list[-1].library_dirs.append(self.SLUlibdir)
- ext_list[-1].libraries.extend(self.superLUFiles)
-+
-
- #Kinsol
- ext_list += cythonize(["assimulo"+os.path.sep+"solvers"+os.path.sep+"kinsol.pyx"],
-@@ -395,7 +420,11 @@
- ext_list[-1].include_dirs = [np.get_include(), "assimulo","assimulo"+os.sep+"lib", self.incdirs]
- ext_list[-1].library_dirs = [self.libdirs]
- ext_list[-1].libraries = ["sundials_kinsol", "sundials_nvecserial"]
--
-+
-+ if self.sundials_with_superlu and self.with_SLU: #If SUNDIALS is compiled with support for SuperLU
-+ ext_list[-1].include_dirs.append(self.SLUincdir)
-+ ext_list[-1].library_dirs.append(self.SLUlibdir)
-+ ext_list[-1].libraries.extend(self.superLUFiles)
-
- for el in ext_list:
- #Debug
-Index: assimulo/solvers/__init__.py
-===================================================================
---- assimulo/solvers/__init__.py (revision 844)
-+++ assimulo/solvers/__init__.py (revision 845)
-@@ -25,7 +25,7 @@
- from .euler import ExplicitEuler
- from .euler import ImplicitEuler
- except ImportError as ie:
-- sys.stderr.write("Could not find {}\n".format(ie.args[0].split("'")[1]))
-+ sys.stderr.write("Could not find " + str(ie) + "\n")
- try:
- from .radau5 import Radau5ODE
- from .radau5 import Radau5DAE
-@@ -32,43 +32,43 @@
- from .radau5 import _Radau5ODE
- from .radau5 import _Radau5DAE
- except ImportError as ie:
-- sys.stderr.write("Could not find {}\n".format(ie.args[0].split("'")[1]))
-+ sys.stderr.write("Could not find " + str(ie) + "\n")
- try:
- from .sundials import IDA
- from .sundials import CVode
- except ImportError as ie:
-- sys.stderr.write("Could not find {}\n".format(ie.args[0].split("'")[1]))
-+ sys.stderr.write("Could not find " + str(ie) + "\n")
- try:
- from .kinsol import KINSOL
- except ImportError as ie:
-- sys.stderr.write("Could not find {}\n".format(ie.args[0].split("'")[1]))
-+ sys.stderr.write("Could not find " + str(ie) + "\n")
- try:
- from .runge_kutta import RungeKutta34
- from .runge_kutta import RungeKutta4
- from .runge_kutta import Dopri5
- except ImportError as ie:
-- sys.stderr.write("Could not find {}\n".format(ie.args[0].split("'")[1]))
-+ sys.stderr.write("Could not find " + str(ie) + "\n")
- try:
- from .rosenbrock import RodasODE
- except ImportError as ie:
-- sys.stderr.write("Could not find {}\n".format(ie.args[0].split("'")[1]))
-+ sys.stderr.write("Could not find " + str(ie) + "\n")
- try:
- from .odassl import ODASSL
- except ImportError as ie:
-- sys.stderr.write("Could not find {}\n".format(ie.args[0].split("'")[1]))
-+ sys.stderr.write("Could not find " + str(ie) + "\n")
- try:
- from .odepack import LSODAR
- except ImportError as ie:
-- sys.stderr.write("Could not find {}\n".format(ie.args[0].split("'")[1]))
-+ sys.stderr.write("Could not find " + str(ie) + "\n")
- try:
- from .radar5 import Radar5ODE
- except ImportError as ie:
-- sys.stderr.write("Could not find {}\n".format(ie.args[0].split("'")[1]))
-+ sys.stderr.write("Could not find " + str(ie) + "\n")
- try:
- from .dasp3 import DASP3ODE
- except ImportError as ie:
-- sys.stderr.write("Could not find {}\n".format(ie.args[0].split("'")[1]))
-+ sys.stderr.write("Could not find " + str(ie) + "\n")
- try:
- from .glimda import GLIMDA
- except ImportError as ie:
-- sys.stderr.write("Could not find {}\n".format(ie.args[0].split("'")[1]))
-+ sys.stderr.write("Could not find " + str(ie) + "\n")
-Index: src/solvers/kinsol.pyx
-===================================================================
---- assimulo/solvers/kinsol.pyx (revision 844)
-+++ assimulo/solvers/kinsol.pyx (revision 845)
-@@ -30,8 +30,8 @@
- cimport sundials_includes as SUNDIALS
-
- #Various C includes transfered to namespace
--from sundials_includes cimport N_Vector, realtype, N_VectorContent_Serial, DENSE_COL
--from sundials_includes cimport memcpy, N_VNew_Serial, DlsMat, SlsMat
-+from sundials_includes cimport N_Vector, realtype, N_VectorContent_Serial, DENSE_COL, sunindextype
-+from sundials_includes cimport memcpy, N_VNew_Serial, DlsMat, SlsMat, SUNMatrix, SUNMatrixContent_Dense, SUNMatrixContent_Sparse
- from sundials_includes cimport malloc, free, realtype, N_VCloneVectorArray_Serial
- from sundials_includes cimport N_VConst_Serial, N_VDestroy_Serial
-
-@@ -57,6 +57,8 @@
-
- cdef object pt_fcn, pt_jac, pt_jacv, pt_prec_setup, pt_prec_solve
- cdef object _added_linear_solver
-+ cdef SUNDIALS.SUNMatrix sun_matrix
-+ cdef SUNDIALS.SUNLinearSolver sun_linearsolver
-
- def __init__(self, problem):
- Algebraic.__init__(self, problem) #Calls the base class
-@@ -86,6 +88,7 @@
- self.options["no_min_epsilon"] = False #Specifies wheter the scaled linear residual is bounded from below
- self.options["max_beta_fails"] = 10
- self.options["max_krylov"] = 0
-+ self.options["precond"] = PREC_NONE
-
- #Statistics
- self.statistics["nfevals"] = 0 #Function evaluations
-@@ -109,6 +112,13 @@
- if self.kinsol_mem != NULL:
- #Free Memory
- SUNDIALS.KINFree(&self.kinsol_mem)
-+
-+ IF SUNDIALS_VERSION >= (3,0,0):
-+ if self.sun_matrix != NULL:
-+ SUNDIALS.SUNMatDestroy(self.sun_matrix)
-+
-+ if self.sun_linearsolver != NULL:
-+ SUNDIALS.SUNLinSolFree(self.sun_linearsolver)
-
- def update_variable_scaling(self, value="Automatic"):
- """
-@@ -170,7 +180,7 @@
-
- cdef initialize_kinsol(self):
- cdef int flag #Used for return
--
-+
- self.y_temp = arr2nv(self.y)
- self.y_scale = arr2nv([1.0]*self.problem_info["dim"])
- self.f_scale = arr2nv([1.0]*self.problem_info["dim"])
-@@ -208,16 +218,34 @@
-
- cpdef add_linear_solver(self):
- if self.options["linear_solver"] == "DENSE":
-- flag = SUNDIALS.KINDense(self.kinsol_mem, self.problem_info["dim"])
-+ IF SUNDIALS_VERSION >= (3,0,0):
-+ #Create a dense Sundials matrix
-+ self.sun_matrix = SUNDIALS.SUNDenseMatrix(self.pData.dim, self.pData.dim)
-+ #Create a dense Sundials linear solver
-+ self.sun_linearsolver = SUNDIALS.SUNDenseLinearSolver(self.y_temp, self.sun_matrix)
-+ #Attach it to Kinsol
-+ flag = SUNDIALS.KINDlsSetLinearSolver(self.kinsol_mem, self.sun_linearsolver, self.sun_matrix)
-+ ELSE:
-+ flag = SUNDIALS.KINDense(self.kinsol_mem, self.problem_info["dim"])
- if flag < 0:
- raise KINSOLError(flag)
-
- if self.problem_info["jac_fcn"]:
-- flag = SUNDIALS.KINDlsSetDenseJacFn(self.kinsol_mem, kin_jac);
-+ IF SUNDIALS_VERSION >= (3,0,0):
-+ flag = SUNDIALS.KINDlsSetJacFn(self.kinsol_mem, kin_jac);
-+ ELSE:
-+ flag = SUNDIALS.KINDlsSetDenseJacFn(self.kinsol_mem, kin_jac);
- if flag < 0:
- raise KINSOLError(flag)
- elif self.options["linear_solver"] == "SPGMR":
-- flag = SUNDIALS.KINSpgmr(self.kinsol_mem, self.options["max_krylov"])
-+ IF SUNDIALS_VERSION >= (3,0,0):
-+ #Create the linear solver
-+ self.sun_linearsolver = SUNDIALS.SUNSPGMR(self.y_temp, self.options["precond"], self.options["max_krylov"])
-+ #Attach it to Kinsol
-+ flag = SUNDIALS.KINSpilsSetLinearSolver(self.kinsol_mem, self.sun_linearsolver)
-+ ELSE:
-+ #Specify the use of KINSpgmr linear solver.
-+ flag = SUNDIALS.KINSpgmr(self.kinsol_mem, self.options["max_krylov"])
- if flag < 0:
- raise KINSOLError(flag)
-
-Index: src/solvers/sundials.pyx
-===================================================================
---- assimulo/solvers/sundials.pyx (revision 844)
-+++ assimulo/solvers/sundials.pyx (revision 845)
-@@ -34,8 +34,8 @@
- cimport sundials_includes as SUNDIALS
-
- #Various C includes transfered to namespace
--from sundials_includes cimport N_Vector, realtype, N_VectorContent_Serial, DENSE_COL
--from sundials_includes cimport memcpy, N_VNew_Serial, DlsMat, SlsMat
-+from sundials_includes cimport N_Vector, realtype, N_VectorContent_Serial, DENSE_COL, sunindextype
-+from sundials_includes cimport memcpy, N_VNew_Serial, DlsMat, SlsMat, SUNMatrix, SUNMatrixContent_Dense, SUNMatrixContent_Sparse
- from sundials_includes cimport malloc, free, realtype, N_VCloneVectorArray_Serial
- from sundials_includes cimport N_VConst_Serial, N_VDestroy_Serial
-
-@@ -70,6 +70,8 @@
- cdef public N.ndarray yS0
- #cdef N.ndarray _event_info
- cdef public N.ndarray g_old
-+ cdef SUNDIALS.SUNMatrix sun_matrix
-+ cdef SUNDIALS.SUNLinearSolver sun_linearsolver
-
- def __init__(self, problem):
- Implicit_ODE.__init__(self, problem) #Calls the base class
-@@ -100,7 +102,9 @@
- self.options["dqrhomax"] = 0.0
- self.options["pbar"] = [1]*self.problem_info["dimSens"]
- self.options["external_event_detection"] = False #Sundials rootfinding is used for event location as default
-+ self.options["precond"] = PREC_NONE
-
-+
- #Solver support
- self.supports["report_continuously"] = True
- self.supports["interpolated_output"] = True
-@@ -173,6 +177,13 @@
- if self.ida_mem != NULL:
- #Free Memory
- SUNDIALS.IDAFree(&self.ida_mem)
-+
-+ IF SUNDIALS_VERSION >= (3,0,0):
-+ if self.sun_matrix != NULL:
-+ SUNDIALS.SUNMatDestroy(self.sun_matrix)
-+
-+ if self.sun_linearsolver != NULL:
-+ SUNDIALS.SUNLinSolFree(self.sun_linearsolver)
-
- cpdef state_event_info(self):
- """
-@@ -255,21 +266,30 @@
- if flag < 0:
- raise IDAError(flag, self.t)
-
-- #Specify the use of the internal dense linear algebra functions.
-- flag = SUNDIALS.IDADense(self.ida_mem, self.pData.dim)
-- if flag < 0:
-- raise IDAError(flag, self.t)
--
-- #Choose a linear solver if and only if NEWTON is choosen
-+ #Choose a linear solver if and only if NEWTON is choosen
- if self.options["linear_solver"] == 'DENSE':
-- #Specify the use of the internal dense linear algebra functions.
-- flag = SUNDIALS.IDADense(self.ida_mem, self.pData.dim)
-+ IF SUNDIALS_VERSION >= (3,0,0):
-+ #Create a dense Sundials matrix
-+ self.sun_matrix = SUNDIALS.SUNDenseMatrix(self.pData.dim, self.pData.dim)
-+ #Create a dense Sundials linear solver
-+ self.sun_linearsolver = SUNDIALS.SUNDenseLinearSolver(self.yTemp, self.sun_matrix)
-+ #Attach it to IDA
-+ flag = SUNDIALS.IDADlsSetLinearSolver(self.ida_mem, self.sun_linearsolver, self.sun_matrix);
-+ ELSE:
-+ #Specify the use of the internal dense linear algebra functions.
-+ flag = SUNDIALS.IDADense(self.ida_mem, self.pData.dim)
- if flag < 0:
- raise IDAError(flag, self.t)
-
- elif self.options["linear_solver"] == 'SPGMR':
-- #Specify the use of SPGMR linear solver.
-- flag = SUNDIALS.IDASpgmr(self.ida_mem, 0) #0 == Default krylov iterations
-+ IF SUNDIALS_VERSION >= (3,0,0):
-+ #Create the linear solver
-+ self.sun_linearsolver = SUNDIALS.SUNSPGMR(self.yTemp, self.options["precond"], 0)
-+ #Attach it to IDAS
-+ flag = SUNDIALS.IDASpilsSetLinearSolver(self.ida_mem, self.sun_linearsolver)
-+ ELSE:
-+ #Specify the use of SPGMR linear solver.
-+ flag = SUNDIALS.IDASpgmr(self.ida_mem, 0) #0 == Default krylov iterations
- if flag < 0:
- raise IDAError(flag, self.t)
-
-@@ -310,12 +330,17 @@
- if self.options["linear_solver"] == 'DENSE':
- #Specify the jacobian to the solver
- if self.pData.JAC != NULL and self.options["usejac"]:
--
-- flag = SUNDIALS.IDADlsSetDenseJacFn(self.ida_mem, ida_jac)
-+ IF SUNDIALS_VERSION >= (3,0,0):
-+ flag = SUNDIALS.IDADlsSetJacFn(self.ida_mem, ida_jac)
-+ ELSE:
-+ flag = SUNDIALS.IDADlsSetDenseJacFn(self.ida_mem, ida_jac)
- if flag < 0:
- raise IDAError(flag,self.t)
- else:
-- flag = SUNDIALS.IDADlsSetDenseJacFn(self.ida_mem, NULL)
-+ IF SUNDIALS_VERSION >= (3,0,0):
-+ flag = SUNDIALS.IDADlsSetJacFn(self.ida_mem, NULL)
-+ ELSE:
-+ flag = SUNDIALS.IDADlsSetDenseJacFn(self.ida_mem, NULL)
- if flag < 0:
- raise IDAError(flag,self.t)
-
-@@ -322,11 +347,17 @@
- elif self.options["linear_solver"] == 'SPGMR':
- #Specify the jacobian times vector function
- if self.pData.JACV != NULL and self.options["usejac"]:
-- flag = SUNDIALS.IDASpilsSetJacTimesVecFn(self.ida_mem, ida_jacv);
-+ IF SUNDIALS_VERSION >= (3,0,0):
-+ flag = SUNDIALS.IDASpilsSetJacTimes(self.ida_mem, SUNDIALS.ida_spils_jtsetup_dummy, ida_jacv);
-+ ELSE:
-+ flag = SUNDIALS.IDASpilsSetJacTimesVecFn(self.ida_mem, ida_jacv);
- if flag < 0:
- raise IDAError(flag, self.t)
- else:
-- flag = SUNDIALS.IDASpilsSetJacTimesVecFn(self.ida_mem, NULL);
-+ IF SUNDIALS_VERSION >= (3,0,0):
-+ flag = SUNDIALS.IDASpilsSetJacTimes(self.ida_mem, NULL, NULL);
-+ ELSE:
-+ flag = SUNDIALS.IDASpilsSetJacTimesVecFn(self.ida_mem, NULL);
- if flag < 0:
- raise IDAError(flag, self.t)
- else:
-@@ -1415,6 +1446,8 @@
- cdef public N.ndarray yS0
- #cdef N.ndarray _event_info
- cdef public N.ndarray g_old
-+ cdef SUNDIALS.SUNMatrix sun_matrix
-+ cdef SUNDIALS.SUNLinearSolver sun_linearsolver
-
- def __init__(self, problem):
- Explicit_ODE.__init__(self, problem) #Calls the base class
-@@ -1479,6 +1512,13 @@
- if self.cvode_mem != NULL:
- #Free Memory
- SUNDIALS.CVodeFree(&self.cvode_mem)
-+
-+ IF SUNDIALS_VERSION >= (3,0,0):
-+ if self.sun_matrix != NULL:
-+ SUNDIALS.SUNMatDestroy(self.sun_matrix)
-+
-+ if self.sun_linearsolver != NULL:
-+ SUNDIALS.SUNLinSolFree(self.sun_linearsolver)
-
- cpdef get_local_errors(self):
- """
-@@ -2019,29 +2059,51 @@
- Updates the simulation options.
- """
- cdef flag
--
-+
- #Choose a linear solver if and only if NEWTON is choosen
- if self.options["linear_solver"] == 'DENSE' and self.options["iter"] == "Newton":
-- #Specify the use of the internal dense linear algebra functions.
-- flag = SUNDIALS.CVDense(self.cvode_mem, self.pData.dim)
-- if flag < 0:
-- raise CVodeError(flag)
-+ IF SUNDIALS_VERSION >= (3,0,0):
-+ #Create a dense Sundials matrix
-+ self.sun_matrix = SUNDIALS.SUNDenseMatrix(self.pData.dim, self.pData.dim)
-+ #Create a dense Sundials linear solver
-+ self.sun_linearsolver = SUNDIALS.SUNDenseLinearSolver(self.yTemp, self.sun_matrix)
-+ #Attach it to CVode
-+ flag = SUNDIALS.CVDlsSetLinearSolver(self.cvode_mem, self.sun_linearsolver, self.sun_matrix);
-+ if flag < 0:
-+ raise CVodeError(flag)
-+ ELSE:
-+ #Specify the use of the internal dense linear algebra functions.
-+ flag = SUNDIALS.CVDense(self.cvode_mem, self.pData.dim)
-+ if flag < 0:
-+ raise CVodeError(flag)
-
- #Specify the jacobian to the solver
- if self.pData.JAC != NULL and self.options["usejac"]:
-- flag = SUNDIALS.CVDlsSetDenseJacFn(self.cvode_mem, cv_jac)
-+ IF SUNDIALS_VERSION >= (3,0,0):
-+ flag = SUNDIALS.CVDlsSetJacFn(self.cvode_mem, cv_jac);
-+ ELSE:
-+ flag = SUNDIALS.CVDlsSetDenseJacFn(self.cvode_mem, cv_jac)
- if flag < 0:
- raise CVodeError(flag)
- else:
-- flag = SUNDIALS.CVDlsSetDenseJacFn(self.cvode_mem, NULL)
-+ IF SUNDIALS_VERSION >= (3,0,0):
-+ flag = SUNDIALS.CVDlsSetJacFn(self.cvode_mem, NULL);
-+ ELSE:
-+ flag = SUNDIALS.CVDlsSetDenseJacFn(self.cvode_mem, NULL)
- if flag < 0:
- raise CVodeError(flag)
-
- elif self.options["linear_solver"] == 'SPGMR' and self.options["iter"] == "Newton":
-- #Specify the use of CVSPGMR linear solver.
-- flag = SUNDIALS.CVSpgmr(self.cvode_mem, self.options["precond"], self.options["maxkrylov"])
-+ IF SUNDIALS_VERSION >= (3,0,0):
-+ #Create the linear solver
-+ self.sun_linearsolver = SUNDIALS.SUNSPGMR(self.yTemp, self.options["precond"], self.options["maxkrylov"])
-+ #Attach it to CVode
-+ flag = SUNDIALS.CVSpilsSetLinearSolver(self.cvode_mem, self.sun_linearsolver)
-+ ELSE:
-+ #Specify the use of CVSPGMR linear solver.
-+ flag = SUNDIALS.CVSpgmr(self.cvode_mem, self.options["precond"], self.options["maxkrylov"])
- if flag < 0:
-- raise CVodeError(flag)
-+ raise CVodeError(flag)
-
- if self.pData.PREC_SOLVE != NULL:
- if self.pData.PREC_SETUP != NULL:
-@@ -2055,11 +2117,17 @@
-
- #Specify the jacobian times vector function
- if self.pData.JACV != NULL and self.options["usejac"]:
-- flag = SUNDIALS.CVSpilsSetJacTimesVecFn(self.cvode_mem, cv_jacv)
-- if flag < 0:
-+ IF SUNDIALS_VERSION >= (3,0,0):
-+ flag = SUNDIALS.CVSpilsSetJacTimes(self.cvode_mem, SUNDIALS.cv_spils_jtsetup_dummy, cv_jacv);
-+ ELSE:
-+ flag = SUNDIALS.CVSpilsSetJacTimesVecFn(self.cvode_mem, cv_jacv)
-+ if flag < 0:
- raise CVodeError(flag)
- else:
-- flag = SUNDIALS.CVSpilsSetJacTimesVecFn(self.cvode_mem, NULL)
-+ IF SUNDIALS_VERSION >= (3,0,0):
-+ flag = SUNDIALS.CVSpilsSetJacTimes(self.cvode_mem, NULL, NULL);
-+ ELSE:
-+ flag = SUNDIALS.CVSpilsSetJacTimesVecFn(self.cvode_mem, NULL)
- if flag < 0:
- raise CVodeError(flag)
- elif self.options["linear_solver"] == 'SPARSE' and self.options["iter"] == "Newton":
-@@ -2066,17 +2134,28 @@
-
- if SUNDIALS.version() < (2,6,0):
- raise AssimuloException("Not supported with this SUNDIALS version.")
--
-+ if SUNDIALS.with_superlu() == 0:
-+ raise AssimuloException("No support for SuperLU was detected, please verify that SuperLU and SUNDIALS has been installed correctly.")
-+
- #Specify the use of CVSPGMR linear solver.
- if self.problem_info["jac_fcn_nnz"] == -1:
- raise AssimuloException("Need to specify the number of non zero elements in the Jacobian via the option 'jac_nnz'")
-- flag = SUNDIALS.CVSuperLUMT(self.cvode_mem, self.options["num_threads"], self.pData.dim, self.problem_info["jac_fcn_nnz"])
-+
-+ IF SUNDIALS_VERSION >= (3,0,0):
-+ self.sun_matrix = SUNDIALS.SUNSparseMatrix(self.pData.dim, self.pData.dim, self.problem_info["jac_fcn_nnz"], CSC_MAT)
-+ self.sun_linearsolver = SUNDIALS.SUNSuperLUMT(self.yTemp, self.sun_matrix, self.options["num_threads"])
-+ flag = SUNDIALS.CVDlsSetLinearSolver(self.cvode_mem, self.sun_linearsolver, self.sun_matrix)
-+ ELSE:
-+ flag = SUNDIALS.CVSuperLUMT(self.cvode_mem, self.options["num_threads"], self.pData.dim, self.problem_info["jac_fcn_nnz"])
- if flag < 0:
- raise CVodeError(flag)
-
- #Specify the jacobian to the solver
- if self.pData.JAC != NULL and self.options["usejac"]:
-- flag = SUNDIALS.CVSlsSetSparseJacFn(self.cvode_mem, cv_jac_sparse)
-+ IF SUNDIALS_VERSION >= (3,0,0):
-+ flag = SUNDIALS.CVDlsSetJacFn(self.cvode_mem, cv_jac_sparse)
-+ ELSE:
-+ flag = SUNDIALS.CVSlsSetSparseJacFn(self.cvode_mem, cv_jac_sparse)
- if flag < 0:
- raise CVodeError(flag)
- else:
-@@ -2899,7 +2978,10 @@
- flag = SUNDIALS.CVSpilsGetNumRhsEvals(self.cvode_mem, &nfevalsLS) #Number of rhs due to jac*vector
- self.statistics["njacvecs"] += njvevals
- elif self.options["linear_solver"] == "SPARSE":
-- flag = SUNDIALS.CVSlsGetNumJacEvals(self.cvode_mem, &njevals)
-+ IF SUNDIALS_VERSION >= (3,0,0):
-+ flag = SUNDIALS.CVDlsGetNumJacEvals(self.cvode_mem, &njevals)
-+ ELSE:
-+ flag = SUNDIALS.CVSlsGetNumJacEvals(self.cvode_mem, &njevals)
- self.statistics["njacs"] += njevals
- else:
- flag = SUNDIALS.CVDlsGetNumJacEvals(self.cvode_mem, &njevals) #Number of jac evals
-Index: assimulo/lib/sundials_includes.pxd
-===================================================================
---- assimulo/lib/sundials_includes.pxd (revision 844)
-+++ assimulo/lib/sundials_includes.pxd (revision 845)
-@@ -37,19 +37,6 @@
- ctypedef double realtype
- ctypedef bint booleantype # should be bool instead of bint, but there is a bug in Cython
-
--#==============================================
--# C headers
--#==============================================
--cdef extern from "string.h":
-- void *memcpy(void *s1, void *s2, int n)
--cdef extern from "stdlib.h":
-- void *malloc(int size)
-- void free(void *ptr)
--
--#==============================================
--#External definitions from Sundials headers
--#==============================================
--
- cdef extern from "sundials/sundials_nvector.h":
- ctypedef _generic_N_Vector* N_Vector
-
-@@ -77,6 +64,107 @@
- void N_VDestroy_Serial(N_Vector v)
- void N_VPrint_Serial(N_Vector v)
-
-+IF SUNDIALS_VERSION >= (3,0,0):
-+ cdef extern from "sundials/sundials_types.h":
-+ IF SUNDIALS_VECTOR_SIZE == "64":
-+ ctypedef long int sunindextype
-+ ELSE:
-+ ctypedef int sunindextype
-+ cdef extern from "sundials/sundials_matrix.h":
-+ ctypedef _generic_SUNMatrix *SUNMatrix;
-+ void SUNMatDestroy(SUNMatrix A);
-+
-+ cdef struct _generic_SUNMatrix_Ops:
-+ SUNMatrix_ID (*getid)(SUNMatrix);
-+ SUNMatrix (*clone)(SUNMatrix);
-+ void (*destroy)(SUNMatrix);
-+ int (*zero)(SUNMatrix);
-+ int (*copy)(SUNMatrix, SUNMatrix);
-+ int (*scaleadd)(realtype, SUNMatrix, SUNMatrix);
-+ int (*scaleaddi)(realtype, SUNMatrix);
-+ int (*matvec)(SUNMatrix, N_Vector, N_Vector);
-+ int (*space)(SUNMatrix, long int*, long int*);
-+
-+ cdef struct _generic_SUNMatrix:
-+ void *content;
-+ _generic_SUNMatrix_Ops *ops;
-+
-+ cdef enum SUNMatrix_ID:
-+ SUNMATRIX_DENSE,
-+ SUNMATRIX_BAND,
-+ SUNMATRIX_SPARSE,
-+ SUNMATRIX_CUSTOM
-+
-+ cdef extern from "sundials/sundials_linearsolver.h":
-+ ctypedef _generic_SUNLinearSolver *SUNLinearSolver;
-+ int SUNLinSolFree(SUNLinearSolver S);
-+
-+ cdef struct _generic_SUNLinearSolver_Ops:
-+ SUNLinearSolver_Type (*gettype)(SUNLinearSolver);
-+ int (*setatimes)(SUNLinearSolver, void*, ATimesFn);
-+ int (*setpreconditioner)(SUNLinearSolver, void*,
-+ PSetupFn, PSolveFn);
-+ int (*setscalingvectors)(SUNLinearSolver,
-+ N_Vector, N_Vector);
-+ int (*initialize)(SUNLinearSolver);
-+ int (*setup)(SUNLinearSolver, SUNMatrix);
-+ int (*solve)(SUNLinearSolver, SUNMatrix, N_Vector,
-+ N_Vector, realtype);
-+ int (*numiters)(SUNLinearSolver);
-+ realtype (*resnorm)(SUNLinearSolver);
-+ long int (*lastflag)(SUNLinearSolver);
-+ int (*space)(SUNLinearSolver, long int*, long int*);
-+ N_Vector (*resid)(SUNLinearSolver);
-+ int (*free)(SUNLinearSolver);
-+
-+ cdef struct _generic_SUNLinearSolver:
-+ void *content;
-+ _generic_SUNLinearSolver_Ops *ops;
-+
-+ cdef enum SUNLinearSolver_Type:
-+ SUNLINEARSOLVER_DIRECT,
-+ SUNLINEARSOLVER_ITERATIVE,
-+ SUNLINEARSOLVER_CUSTOM
-+
-+ cdef extern from "sunmatrix/sunmatrix_dense.h":
-+ ctypedef _SUNMatrixContent_Dense *SUNMatrixContent_Dense;
-+ cdef struct _SUNMatrixContent_Dense:
-+ sunindextype M;
-+ sunindextype N;
-+ realtype *data;
-+ sunindextype ldata;
-+ realtype **cols;
-+ SUNMatrix SUNDenseMatrix(sunindextype M, sunindextype N);
-+ cdef extern from "sunmatrix/sunmatrix_sparse.h":
-+ ctypedef _SUNMatrixContent_Sparse *SUNMatrixContent_Sparse;
-+ cdef struct _SUNMatrixContent_Sparse:
-+ sunindextype M;
-+ sunindextype N;
-+ sunindextype NNZ;
-+ sunindextype NP;
-+ realtype *data;
-+ int sparsetype;
-+ sunindextype *indexvals;
-+ sunindextype *indexptrs;
-+ sunindextype **rowvals;
-+ sunindextype **colptrs;
-+ sunindextype **colvals;
-+ sunindextype **rowptrs;
-+ SUNMatrix SUNSparseMatrix(sunindextype M, sunindextype N, sunindextype NNZ, int sparsetype);
-+ cdef extern from "sunlinsol/sunlinsol_dense.h":
-+ SUNLinearSolver SUNDenseLinearSolver(N_Vector y, SUNMatrix A);
-+ cdef extern from "sunlinsol/sunlinsol_spgmr.h":
-+ SUNLinearSolver SUNSPGMR(N_Vector y, int pretype, int maxl);
-+
-+ELSE:
-+ #Dummy defines
-+ ctypedef void *SUNLinearSolver
-+ ctypedef void *SUNMatrix
-+ ctypedef void *SUNMatrixContent_Dense
-+ ctypedef void *SUNMatrixContent_Sparse
-+ ctypedef int sunindextype
-+
-+
- #Struct for handling the Jacobian data
- cdef extern from "sundials/sundials_direct.h":
- cdef struct _DlsMat:
-@@ -92,7 +180,7 @@
- realtype **cols
- ctypedef _DlsMat* DlsMat
- cdef realtype* DENSE_COL(DlsMat A, int j)
--
-+
- IF SUNDIALS_VERSION >= (2,6,3):
- cdef extern from "sundials/sundials_sparse.h":
- cdef struct _SlsMat:
-@@ -128,7 +216,26 @@
- int *rowvals
- int *colptrs
- ctypedef _SlsMat* SlsMat
-+
-+#==============================================
-+# C headers
-+#==============================================
-+cdef extern from "string.h":
-+ void *memcpy(void *s1, void *s2, int n)
-+cdef extern from "stdlib.h":
-+ void *malloc(int size)
-+ void free(void *ptr)
-+
-+#==============================================
-+#External definitions from Sundials headers
-+#==============================================
-
-+IF SUNDIALS_WITH_SUPERLU:
-+ cdef inline int with_superlu(): return 1
-+ELSE:
-+ cdef inline int with_superlu(): return 0
-+
-+
- cdef extern from "cvodes/cvodes.h":
- void* CVodeCreate(int lmm, int iter)
- ctypedef int (*CVRhsFn)(realtype t, N_Vector y, N_Vector ydot, void *f_data)
-@@ -225,45 +332,67 @@
- int CVodeGetSensNonlinSolvStats(void *cvode_mem, long int *nSniters, long int *nSncfails)
- int CVodeGetStgrSensNumNonlinSolvIters(void *cvode_mem, long int *nSTGR1niters)
- int CVodeGetStgrSensNumNonlinSolvConvFails(void *cvode_mem, long int *nSTGR1ncfails)
-+
-+cdef extern from "cvodes/cvodes_spils.h":
-+ ctypedef int (*CVSpilsJacTimesVecFn)(N_Vector v, N_Vector Jv, realtype t,
-+ N_Vector y, N_Vector fy, void *user_data, N_Vector tmp)
-+
-+IF SUNDIALS_VERSION >= (3,0,0):
-+ cdef extern from "cvodes/cvodes_direct.h":
-+ ctypedef int (*CVDlsDenseJacFn)(realtype t, N_Vector y, N_Vector fy,
-+ SUNMatrix Jac, void *user_data, N_Vector tmp1, N_Vector tmp2, N_Vector tmp3)
-+ int CVDlsSetLinearSolver(void *cvode_mem, SUNLinearSolver LS, SUNMatrix A);
-+ int CVDlsSetJacFn(void *cvode_mem, CVDlsDenseJacFn djac)
-+ cdef extern from "cvodes/cvodes_spils.h":
-+ int CVSpilsSetLinearSolver(void *cvode_mem, SUNLinearSolver LS);
-+ ctypedef int (*CVSpilsJacTimesSetupFn)(realtype t, N_Vector y, N_Vector fy, void *user_data);
-+ int CVSpilsSetJacTimes(void *cvode_mem, CVSpilsJacTimesSetupFn jtsetup, CVSpilsJacTimesVecFn jtimes);
-
-
--cdef extern from "cvodes/cvodes_dense.h":
-- int CVDense(void *cvode_mem, long int n)
-- ctypedef int (*CVDlsDenseJacFn)(int n, realtype t, N_Vector y, N_Vector fy,
-- DlsMat Jac, void *user_data, N_Vector tmp1, N_Vector tmp2, N_Vector tmp3)
-- int CVDlsSetDenseJacFn(void *cvode_mem, CVDlsDenseJacFn djac)
-+ IF SUNDIALS_WITH_SUPERLU:
-+ cdef extern from "sunlinsol/sunlinsol_superlumt.h":
-+ SUNLinearSolver SUNSuperLUMT(N_Vector y, SUNMatrix A, int num_threads)
-+ ELSE:
-+ cdef inline SUNLinearSolver SUNSuperLUMT(N_Vector y, SUNMatrix A, int num_threads): return NULL
-+
-+ cdef inline int cv_spils_jtsetup_dummy(realtype t, N_Vector y, N_Vector fy, void *user_data): return 0
-+ cdef inline tuple version(): return (3,0,0)
-+ELSE:
-+ cdef extern from "cvodes/cvodes_dense.h":
-+ int CVDense(void *cvode_mem, long int n)
-+ ctypedef int (*CVDlsDenseJacFn)(int n, realtype t, N_Vector y, N_Vector fy,
-+ DlsMat Jac, void *user_data, N_Vector tmp1, N_Vector tmp2, N_Vector tmp3)
-+ int CVDlsSetDenseJacFn(void *cvode_mem, CVDlsDenseJacFn djac)
-
--cdef extern from "cvodes/cvodes_spgmr.h":
-- int CVSpgmr(void *cvode_mem, int pretype, int max1)
-+ cdef extern from "cvodes/cvodes_spgmr.h":
-+ int CVSpgmr(void *cvode_mem, int pretype, int max1)
-
--IF SUNDIALS_VERSION >= (2,6,0):
-- cdef extern from "cvodes/cvodes_sparse.h":
-+ cdef extern from "cvodes/cvodes_spils.h":
-+ int CVSpilsSetJacTimesVecFn(void *cvode_mem, CVSpilsJacTimesVecFn jtv)
-+
-+ IF SUNDIALS_VERSION >= (2,6,0):
-+ cdef extern from "cvodes/cvodes_sparse.h":
-+ ctypedef int (*CVSlsSparseJacFn)(realtype t, N_Vector y, N_Vector fy,
-+ SlsMat Jac, void *user_data, N_Vector tmp1,
-+ N_Vector tmp2, N_Vector tmp3)
-+ int CVSlsSetSparseJacFn(void *cvode_mem, CVSlsSparseJacFn jac)
-+ int CVSlsGetNumJacEvals(void *cvode_mem, long int *njevals)
-+ cdef inline tuple version(): return (2,6,0)
-+ IF SUNDIALS_WITH_SUPERLU:
-+ cdef extern from "cvodes/cvodes_sparse.h":
-+ int CVSuperLUMT(void *cvode_mem, int numthreads, int n, int nnz)
-+ ELSE:
-+ cdef inline int CVSuperLUMT(void *cvode_mem, int numthreads, int n, int nnz): return -1
-+ ELSE:
-+ cdef inline int CVSuperLUMT(void *cvode_mem, int numthreads, int n, int nnz): return -1
- ctypedef int (*CVSlsSparseJacFn)(realtype t, N_Vector y, N_Vector fy,
- SlsMat Jac, void *user_data, N_Vector tmp1,
- N_Vector tmp2, N_Vector tmp3)
-- int CVSlsSetSparseJacFn(void *cvode_mem, CVSlsSparseJacFn jac)
-- int CVSlsGetNumJacEvals(void *cvode_mem, long int *njevals)
-- #cdef inline char* version(): return "2.6.0"
-- cdef inline tuple version(): return (2,6,0)
-- IF SUNDIALS_WITH_SUPERLU:
-- cdef extern from "cvodes/cvodes_sparse.h":
-- int CVSuperLUMT(void *cvode_mem, int numthreads, int n, int nnz)
-- ELSE:
-- cdef inline int CVSuperLUMT(void *cvode_mem, int numthreads, int n, int nnz): return -1
--ELSE:
-- cdef inline int CVSuperLUMT(void *cvode_mem, int numthreads, int n, int nnz): return -1
-- ctypedef int (*CVSlsSparseJacFn)(realtype t, N_Vector y, N_Vector fy,
-- SlsMat Jac, void *user_data, N_Vector tmp1,
-- N_Vector tmp2, N_Vector tmp3)
-- cdef inline int CVSlsSetSparseJacFn(void *cvode_mem, CVSlsSparseJacFn jac): return -1
-- cdef inline int CVSlsGetNumJacEvals(void *cvode_mem, long int *njevals): return -1
-- #cdef inline char* version(): return "2.5.0"
-- cdef inline tuple version(): return (2,5,0)
-+ cdef inline int CVSlsSetSparseJacFn(void *cvode_mem, CVSlsSparseJacFn jac): return -1
-+ cdef inline int CVSlsGetNumJacEvals(void *cvode_mem, long int *njevals): return -1
-+ cdef inline tuple version(): return (2,5,0)
-
- cdef extern from "cvodes/cvodes_spils.h":
-- ctypedef int (*CVSpilsJacTimesVecFn)(N_Vector v, N_Vector Jv, realtype t,
-- N_Vector y, N_Vector fy,
-- void *user_data, N_Vector tmp)
- ctypedef int (*CVSpilsPrecSetupFn)(realtype t, N_Vector y, N_Vector fy,
- booleantype jok, booleantype *jcurPtr,
- realtype gamma, void *user_data,
-@@ -273,13 +402,12 @@
- N_Vector r, N_Vector z,
- realtype gamma, realtype delta,
- int lr, void *user_data, N_Vector tmp)
-- int CVSpilsSetJacTimesVecFn(void *cvode_mem, CVSpilsJacTimesVecFn jtv)
-+
- int CVSpilsSetPreconditioner(void *cvode_mem, CVSpilsPrecSetupFn psetup, CVSpilsPrecSolveFn psolve)
- int CVSpilsGetNumJtimesEvals(void *cvode_mem, long int *njvevals) #Number of jac*vector evals
- int CVSpilsGetNumRhsEvals(void *cvode_mem, long int *nfevalsLS) #Number of res evals due to jacÄvector evals
- int CVSpilsGetNumPrecEvals(void *cvode_mem, long int *npevals)
- int CVSpilsGetNumPrecSolves(void *cvode_mem, long int *npsolves)
--
-
- cdef extern from "idas/idas.h":
- ctypedef int (*IDAResFn)(realtype tt, N_Vector yy, N_Vector yp, N_Vector rr, void *user_data)
-@@ -380,19 +508,40 @@
-
- #End Sensitivities
- #=================
--
--cdef extern from "idas/idas_dense.h":
-- int IDADense(void *ida_mem, long int n)
-- ctypedef int (*IDADlsDenseJacFn)(int Neq, realtype tt, realtype cj, N_Vector yy,
-- N_Vector yp, N_Vector rr, DlsMat Jac, void *user_data,
-- N_Vector tmp1, N_Vector tmp2, N_Vector tmp3)
-- int IDADlsSetDenseJacFn(void *ida_mem, IDADlsDenseJacFn djac)
-
--cdef extern from "idas/idas_dense.h":
-- int IDASpgmr(void *ida_mem, int max1)
-- ctypedef int (*IDASpilsJacTimesVecFn)(realtype tt, N_Vector yy, N_Vector yp, N_Vector rr, N_Vector v, N_Vector Jv, realtype cj, void *user_data,N_Vector tmp1, N_Vector tmp2)
-- int IDASpilsSetJacTimesVecFn(void *ida_mem, IDASpilsJacTimesVecFn ida_jacv)
-+cdef extern from "idas/idas_spils.h":
-+ ctypedef int (*IDASpilsJacTimesVecFn)(realtype tt, N_Vector yy, N_Vector yp, N_Vector rr,
-+ N_Vector v, N_Vector Jv, realtype cj, void *user_data,N_Vector tmp1, N_Vector tmp2)
-
-+IF SUNDIALS_VERSION >= (3,0,0):
-+ cdef extern from "idas/idas_direct.h":
-+ ctypedef int (*IDADlsDenseJacFn)(realtype tt, realtype cj, N_Vector yy,
-+ N_Vector yp, N_Vector rr, SUNMatrix Jac, void *user_data,
-+ N_Vector tmp1, N_Vector tmp2, N_Vector tmp3)
-+ int IDADlsSetJacFn(void *ida_mem, IDADlsDenseJacFn djac)
-+ int IDADlsSetLinearSolver(void *ida_mem, SUNLinearSolver LS, SUNMatrix A);
-+
-+ cdef extern from "idas/idas_spils.h":
-+ int IDASpilsSetLinearSolver(void *ida_mem, SUNLinearSolver LS);
-+ ctypedef int (*IDASpilsJacTimesSetupFn)(realtype tt, N_Vector yy,
-+ N_Vector yp, N_Vector rr, realtype c_j, void *user_data);
-+ int IDASpilsSetJacTimes(void *ida_mem,
-+ IDASpilsJacTimesSetupFn jtsetup, IDASpilsJacTimesVecFn jtimes);
-+
-+ cdef inline int ida_spils_jtsetup_dummy(realtype tt, N_Vector yy, N_Vector yp, N_Vector rr, realtype c_j, void *user_data): return 0
-+ELSE:
-+ cdef extern from "idas/idas_dense.h":
-+ int IDADense(void *ida_mem, long int n)
-+ ctypedef int (*IDADlsDenseJacFn)(int Neq, realtype tt, realtype cj, N_Vector yy,
-+ N_Vector yp, N_Vector rr, DlsMat Jac, void *user_data,
-+ N_Vector tmp1, N_Vector tmp2, N_Vector tmp3)
-+ int IDADlsSetDenseJacFn(void *ida_mem, IDADlsDenseJacFn djac)
-+
-+ int IDASpgmr(void *ida_mem, int max1)
-+
-+ cdef extern from "idas/idas_spils.h":
-+ int IDASpilsSetJacTimesVecFn(void *ida_mem, IDASpilsJacTimesVecFn ida_jacv)
-+
- cdef extern from "idas/idas_spils.h":
- int IDASpilsGetNumJtimesEvals(void *ida_mem, long int *njvevals) #Number of jac*vector
- int IDASpilsGetNumResEvals(void *ida_mem, long int *nfevalsLS) #Number of rhs due to jac*vector
-@@ -455,14 +604,31 @@
- # fuction used to deallocate memory used by KINSOL
- void KINFree(void **kinmem)
-
--# functions used for supplying jacobian, and receiving info from linear solver
--cdef extern from "kinsol/kinsol_direct.h":
-- # user functions
-- ctypedef int (*KINDlsDenseJacFn)(int dim, N_Vector u, N_Vector fu, DlsMat J, void *user_data, N_Vector tmp1, N_Vector tmp2)
-+
-+IF SUNDIALS_VERSION >= (3,0,0):
-+ cdef extern from "kinsol/kinsol_direct.h":
-+ ctypedef int (*KINDlsDenseJacFn)(N_Vector u, N_Vector fu, SUNMatrix J, void *user_data, N_Vector tmp1, N_Vector tmp2)
-+ int KINDlsSetLinearSolver(void *kinmem, SUNLinearSolver LS, SUNMatrix A);
-+ int KINDlsSetJacFn(void *kinmem, KINDlsDenseJacFn djac)
-
-- # function used to link user functions to KINSOL
-- int KINDlsSetDenseJacFn(void *kinmem, KINDlsDenseJacFn jac)
-+ cdef extern from "kinsol/kinsol_spils.h":
-+ int KINSpilsSetLinearSolver(void *kinsol_mem, SUNLinearSolver LS);
-+ELSE:
-+ # functions used for supplying jacobian, and receiving info from linear solver
-+ cdef extern from "kinsol/kinsol_direct.h":
-+ # user functions
-+ ctypedef int (*KINDlsDenseJacFn)(int dim, N_Vector u, N_Vector fu, DlsMat J, void *user_data, N_Vector tmp1, N_Vector tmp2)
-+
-+ # function used to link user functions to KINSOL
-+ int KINDlsSetDenseJacFn(void *kinmem, KINDlsDenseJacFn jac)
-+
-+ cdef extern from "kinsol/kinsol_dense.h":
-+ int KINDense(void *kinmem, int dim)
-+
-+ cdef extern from "kinsol/kinsol_spgmr.h":
-+ int KINSpgmr(void *kinmem, int maxl)
-
-+cdef extern from "kinsol/kinsol_direct.h":
- # optional output fcts for linear direct solver
- int KINDlsGetWorkSpace(void *kinmem, long int *lenrwB, long int *leniwB)
- int KINDlsGetNumJacEvals(void *kinmem, long int *njevalsB)
-@@ -470,12 +636,6 @@
- int KINDlsGetLastFlag(void *kinmem, int *flag)
- char *KINDlsGetReturnFlagName(int flag)
-
--cdef extern from "kinsol/kinsol_dense.h":
-- int KINDense(void *kinmem, int dim)
--
--cdef extern from "kinsol/kinsol_spgmr.h":
-- int KINSpgmr(void *kinmem, int maxl)
--
- cdef extern from "kinsol/kinsol_spils.h":
- ctypedef int (*KINSpilsJacTimesVecFn)(N_Vector vv, N_Vector Jv, N_Vector vx, bint new_u,
- void *problem_data)
-Index: src/lib/sundials_constants.pxi
-===================================================================
---- assimulo/lib/sundials_constants.pxi (revision 844)
-+++ assimulo/lib/sundials_constants.pxi (revision 845)
-@@ -95,6 +95,9 @@
- DEF CV_BAD_IS = -45
-
-
-+DEF CSC_MAT = 0
-+DEF CSR_MAT = 1
-+
- #==========
- # IDA
- #==========
-Index: src/lib/sundials_callbacks.pxi
-===================================================================
---- assimulo/lib/sundials_callbacks.pxi (revision 844)
-+++ assimulo/lib/sundials_callbacks.pxi (revision 845)
-@@ -94,98 +94,55 @@
- traceback.print_exc()
- return CV_UNREC_RHSFUNC_ERR
-
--@cython.boundscheck(False)
--@cython.wraparound(False)
--cdef int cv_jac_sparse(realtype t, N_Vector yv, N_Vector fy, SlsMat Jacobian,
-- void *problem_data, N_Vector tmp1, N_Vector tmp2, N_Vector tmp3):
-- """
-- This method is used to connect the Assimulo.Problem.jac to the Sundials
-- Sparse Jacobian function.
-- """
-- cdef ProblemData pData = <ProblemData>problem_data
-- #cdef N.ndarray y = nv2arr(yv)
-- cdef N.ndarray y = pData.work_y
-- cdef int i
-- cdef int nnz = Jacobian.NNZ
-- cdef int ret_nnz
-- cdef int dim = Jacobian.N
-- cdef realtype* data = Jacobian.data
--
-- IF SUNDIALS_VERSION >= (2,6,3):
-- cdef int* rowvals = Jacobian.rowvals[0]
-- cdef int* colptrs = Jacobian.colptrs[0]
-- ELSE:
-- cdef int* rowvals = Jacobian.rowvals
-- cdef int* colptrs = Jacobian.colptrs
--
-- nv2arr_inplace(yv, y)
-- """
-- realtype *data;
-- int *rowvals;
-- int *colptrs;
-- """
-- try:
-- if pData.dimSens > 0: #Sensitivity activated
-- p = realtype2arr(pData.p,pData.dimSens)
-- if pData.sw != NULL:
-- jac=(<object>pData.JAC)(t,y,p=p,sw=<list>pData.sw)
-- else:
-- jac=(<object>pData.JAC)(t,y,p=p)
-- else:
-- if pData.sw != NULL:
-- jac=(<object>pData.JAC)(t,y,sw=<list>pData.sw)
-- else:
-- jac=(<object>pData.JAC)(t,y)
--
-- if not isinstance(jac, sparse.csc.csc_matrix):
-- jac = sparse.csc.csc_matrix(jac)
-- raise AssimuloException("The Jacobian must be stored on Scipy's CSC format.")
-- ret_nnz = jac.nnz
-- if ret_nnz > nnz:
-- raise AssimuloException("The Jacobian has more entries than supplied to the problem class via 'jac_nnz'")
-
-- for i in range(min(ret_nnz,nnz)):
-- data[i] = jac.data[i]
-- rowvals[i] = jac.indices[i]
-- for i in range(dim+1):
-- colptrs[i] = jac.indptr[i]
-+IF SUNDIALS_VERSION >= (3,0,0):
-+ @cython.boundscheck(False)
-+ @cython.wraparound(False)
-+ cdef int cv_jac_sparse(realtype t, N_Vector yv, N_Vector fy, SUNMatrix Jac,
-+ void *problem_data, N_Vector tmp1, N_Vector tmp2, N_Vector tmp3):
-+ """
-+ This method is used to connect the Assimulo.Problem.jac to the Sundials
-+ Sparse Jacobian function.
-+ """
-+ cdef ProblemData pData = <ProblemData>problem_data
-+ cdef SUNMatrixContent_Sparse Jacobian = <SUNMatrixContent_Sparse>Jac.content
-+ cdef N.ndarray y = pData.work_y
-+ cdef int i
-+ cdef sunindextype nnz = Jacobian.NNZ
-+ cdef int ret_nnz
-+ cdef sunindextype dim = Jacobian.N
-+ cdef realtype* data = Jacobian.data
-+ cdef sunindextype* rowvals = Jacobian.rowvals[0]
-+ cdef sunindextype* colptrs = Jacobian.colptrs[0]
-
-- return CVDLS_SUCCESS
-- except(N.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError):
-- return CVDLS_JACFUNC_RECVR #Recoverable Error (See Sundials description)
-- except:
-- traceback.print_exc()
-- return CVDLS_JACFUNC_UNRECVR
-+ nv2arr_inplace(yv, y)
-
--cdef int cv_jac(int Neq, realtype t, N_Vector yv, N_Vector fy, DlsMat Jacobian,
-- void *problem_data, N_Vector tmp1, N_Vector tmp2, N_Vector tmp3):
-- """
-- This method is used to connect the Assimulo.Problem.jac to the Sundials
-- Jacobian function.
-- """
-- cdef ProblemData pData = <ProblemData>problem_data
-- #cdef ndarray[realtype, ndim=2, mode='c'] jac #Used for return from the user function
-- cdef realtype* col_i=DENSE_COL(Jacobian,0)
-- #(<ndarray>pData.y).data = <realtype*>((<N_VectorContent_Serial>yv.content).data)
-- #cdef N.ndarray y = nv2arr(yv)
-- cdef N.ndarray y = pData.work_y
-- cdef int i,j
--
-- nv2arr_inplace(yv, y)
--
-- if pData.dimSens>0: #Sensitivity activated
-- p = realtype2arr(pData.p,pData.dimSens)
- try:
-- if pData.sw != NULL:
-- jac=(<object>pData.JAC)(t,y,sw=<list>pData.sw,p=p)
-+ if pData.dimSens > 0: #Sensitivity activated
-+ p = realtype2arr(pData.p,pData.dimSens)
-+ if pData.sw != NULL:
-+ jac=(<object>pData.JAC)(t,y,p=p,sw=<list>pData.sw)
-+ else:
-+ jac=(<object>pData.JAC)(t,y,p=p)
- else:
-- jac=(<object>pData.JAC)(t,y,p)
-+ if pData.sw != NULL:
-+ jac=(<object>pData.JAC)(t,y,sw=<list>pData.sw)
-+ else:
-+ jac=(<object>pData.JAC)(t,y)
-
-- for i in range(Neq):
-- col_i = DENSE_COL(Jacobian, i)
-- for j in range(Neq):
-- col_i[j] = jac[j,i]
-+ if not isinstance(jac, sparse.csc.csc_matrix):
-+ jac = sparse.csc.csc_matrix(jac)
-+ raise AssimuloException("The Jacobian must be stored on Scipy's CSC format.")
-+ ret_nnz = jac.nnz
-+ if ret_nnz > nnz:
-+ raise AssimuloException("The Jacobian has more entries than supplied to the problem class via 'jac_nnz'")
-
-+ for i in range(min(ret_nnz,nnz)):
-+ data[i] = jac.data[i]
-+ rowvals[i] = jac.indices[i]
-+ for i in range(dim+1):
-+ colptrs[i] = jac.indptr[i]
-+
- return CVDLS_SUCCESS
- except(N.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError):
- return CVDLS_JACFUNC_RECVR #Recoverable Error (See Sundials description)
-@@ -192,18 +149,62 @@
- except:
- traceback.print_exc()
- return CVDLS_JACFUNC_UNRECVR
-- else:
-+ELSE:
-+ @cython.boundscheck(False)
-+ @cython.wraparound(False)
-+ cdef int cv_jac_sparse(realtype t, N_Vector yv, N_Vector fy, SlsMat Jacobian,
-+ void *problem_data, N_Vector tmp1, N_Vector tmp2, N_Vector tmp3):
-+ """
-+ This method is used to connect the Assimulo.Problem.jac to the Sundials
-+ Sparse Jacobian function.
-+ """
-+ cdef ProblemData pData = <ProblemData>problem_data
-+ cdef N.ndarray y = pData.work_y
-+ cdef int i
-+ cdef int nnz = Jacobian.NNZ
-+ cdef int ret_nnz
-+ cdef int dim = Jacobian.N
-+ cdef realtype* data = Jacobian.data
-+
-+ IF SUNDIALS_VERSION >= (2,6,3):
-+ cdef int* rowvals = Jacobian.rowvals[0]
-+ cdef int* colptrs = Jacobian.colptrs[0]
-+ ELSE:
-+ cdef int* rowvals = Jacobian.rowvals
-+ cdef int* colptrs = Jacobian.colptrs
-+
-+ nv2arr_inplace(yv, y)
-+ """
-+ realtype *data;
-+ int *rowvals;
-+ int *colptrs;
-+ """
- try:
-- if pData.sw != NULL:
-- jac=(<object>pData.JAC)(t,y,sw=<list>pData.sw)
-+ if pData.dimSens > 0: #Sensitivity activated
-+ p = realtype2arr(pData.p,pData.dimSens)
-+ if pData.sw != NULL:
-+ jac=(<object>pData.JAC)(t,y,p=p,sw=<list>pData.sw)
-+ else:
-+ jac=(<object>pData.JAC)(t,y,p=p)
- else:
-- jac=(<object>pData.JAC)(t,y)
--
-- for i in range(Neq):
-- col_i = DENSE_COL(Jacobian, i)
-- for j in range(Neq):
-- col_i[j] = jac[j,i]
-+ if pData.sw != NULL:
-+ jac=(<object>pData.JAC)(t,y,sw=<list>pData.sw)
-+ else:
-+ jac=(<object>pData.JAC)(t,y)
-+
-+ if not isinstance(jac, sparse.csc.csc_matrix):
-+ jac = sparse.csc.csc_matrix(jac)
-+ raise AssimuloException("The Jacobian must be stored on Scipy's CSC format.")
-+ ret_nnz = jac.nnz
-+ if ret_nnz > nnz:
-+ raise AssimuloException("The Jacobian has more entries than supplied to the problem class via 'jac_nnz'")
-
-+ for i in range(min(ret_nnz,nnz)):
-+ data[i] = jac.data[i]
-+ rowvals[i] = jac.indices[i]
-+ for i in range(dim+1):
-+ colptrs[i] = jac.indptr[i]
-+
- return CVDLS_SUCCESS
- except(N.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError):
- return CVDLS_JACFUNC_RECVR #Recoverable Error (See Sundials description)
-@@ -210,7 +211,112 @@
- except:
- traceback.print_exc()
- return CVDLS_JACFUNC_UNRECVR
-+
-+
-+IF SUNDIALS_VERSION >= (3,0,0):
-+ cdef int cv_jac(realtype t, N_Vector yv, N_Vector fy, SUNMatrix Jac,
-+ void *problem_data, N_Vector tmp1, N_Vector tmp2, N_Vector tmp3):
-+ """
-+ This method is used to connect the Assimulo.Problem.jac to the Sundials
-+ Jacobian function.
-+ """
-+ cdef SUNMatrixContent_Dense Jacobian = <SUNMatrixContent_Dense>Jac.content
-+ cdef ProblemData pData = <ProblemData>problem_data
-+ cdef realtype* col_i=Jacobian.cols[0]
-+ cdef N.ndarray y = pData.work_y
-+ cdef int i,j, Neq = pData.dim
-
-+ nv2arr_inplace(yv, y)
-+
-+ if pData.dimSens>0: #Sensitivity activated
-+ p = realtype2arr(pData.p,pData.dimSens)
-+ try:
-+ if pData.sw != NULL:
-+ jac=(<object>pData.JAC)(t,y,sw=<list>pData.sw,p=p)
-+ else:
-+ jac=(<object>pData.JAC)(t,y,p)
-+
-+ for i in range(Neq):
-+ col_i = Jacobian.cols[i]
-+ for j in range(Neq):
-+ col_i[j] = jac[j,i]
-+
-+ return CVDLS_SUCCESS
-+ except(N.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError):
-+ return CVDLS_JACFUNC_RECVR #Recoverable Error (See Sundials description)
-+ except:
-+ traceback.print_exc()
-+ return CVDLS_JACFUNC_UNRECVR
-+ else:
-+ try:
-+ if pData.sw != NULL:
-+ jac=(<object>pData.JAC)(t,y,sw=<list>pData.sw)
-+ else:
-+ jac=(<object>pData.JAC)(t,y)
-+
-+ for i in range(Neq):
-+ col_i = Jacobian.cols[i]
-+ for j in range(Neq):
-+ col_i[j] = jac[j,i]
-+
-+ return CVDLS_SUCCESS
-+ except(N.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError):
-+ return CVDLS_JACFUNC_RECVR #Recoverable Error (See Sundials description)
-+ except:
-+ traceback.print_exc()
-+ return CVDLS_JACFUNC_UNRECVR
-+ELSE:
-+ cdef int cv_jac(int Neq, realtype t, N_Vector yv, N_Vector fy, DlsMat Jacobian,
-+ void *problem_data, N_Vector tmp1, N_Vector tmp2, N_Vector tmp3):
-+ """
-+ This method is used to connect the Assimulo.Problem.jac to the Sundials
-+ Jacobian function.
-+ """
-+ cdef ProblemData pData = <ProblemData>problem_data
-+ cdef realtype* col_i=DENSE_COL(Jacobian,0)
-+ cdef N.ndarray y = pData.work_y
-+ cdef int i,j
-+
-+ nv2arr_inplace(yv, y)
-+
-+ if pData.dimSens>0: #Sensitivity activated
-+ p = realtype2arr(pData.p,pData.dimSens)
-+ try:
-+ if pData.sw != NULL:
-+ jac=(<object>pData.JAC)(t,y,sw=<list>pData.sw,p=p)
-+ else:
-+ jac=(<object>pData.JAC)(t,y,p)
-+
-+ for i in range(Neq):
-+ col_i = DENSE_COL(Jacobian, i)
-+ for j in range(Neq):
-+ col_i[j] = jac[j,i]
-+
-+ return CVDLS_SUCCESS
-+ except(N.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError):
-+ return CVDLS_JACFUNC_RECVR #Recoverable Error (See Sundials description)
-+ except:
-+ traceback.print_exc()
-+ return CVDLS_JACFUNC_UNRECVR
-+ else:
-+ try:
-+ if pData.sw != NULL:
-+ jac=(<object>pData.JAC)(t,y,sw=<list>pData.sw)
-+ else:
-+ jac=(<object>pData.JAC)(t,y)
-+
-+ for i in range(Neq):
-+ col_i = DENSE_COL(Jacobian, i)
-+ for j in range(Neq):
-+ col_i[j] = jac[j,i]
-+
-+ return CVDLS_SUCCESS
-+ except(N.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError):
-+ return CVDLS_JACFUNC_RECVR #Recoverable Error (See Sundials description)
-+ except:
-+ traceback.print_exc()
-+ return CVDLS_JACFUNC_UNRECVR
-+
- cdef int cv_jacv(N_Vector vv, N_Vector Jv, realtype t, N_Vector yv, N_Vector fyv,
- void *problem_data, N_Vector tmp):
- """
-@@ -337,9 +443,6 @@
- Root-finding function.
- """
- cdef ProblemData pData = <ProblemData>problem_data
-- #cdef ndarray[realtype, ndim=1, mode='c'] root #Used for return from the user function
-- #(<ndarray>pData.y).data = <realtype*>((<N_VectorContent_Serial>yv.content).data)
-- #cdef N.ndarray y = nv2arr(yv)
- cdef N.ndarray y = pData.work_y
- cdef int i
-
-@@ -366,12 +469,8 @@
- """
- cdef ProblemData pData = <ProblemData>problem_data
- cdef N.ndarray[realtype, ndim=1, mode='c'] res #Used for return from the user function
-- #(<ndarray>pData.y).data = <realtype*>((<N_VectorContent_Serial>yv.content).data)
-- #(<ndarray>pData.yd).data = <realtype*>((<N_VectorContent_Serial>yvdot.content).data)
- cdef N.ndarray y = pData.work_y
- cdef N.ndarray yd = pData.work_yd
-- # cdef N.ndarray y = nv2arr(yv)
-- # cdef N.ndarray yd = nv2arr(yvdot)
- cdef realtype* resptr=(<N_VectorContent_Serial>residual.content).data
- cdef int i
-
-@@ -414,63 +513,113 @@
- except:
- traceback.print_exc()
- return IDA_RES_FAIL
--
--cdef int ida_jac(int Neq, realtype t, realtype c, N_Vector yv, N_Vector yvdot, N_Vector residual, DlsMat Jacobian,
-+
-+IF SUNDIALS_VERSION >= (3,0,0):
-+ cdef int ida_jac(realtype t, realtype c, N_Vector yv, N_Vector yvdot, N_Vector residual, SUNMatrix Jac,
-+ void *problem_data, N_Vector tmp1, N_Vector tmp2, N_Vector tmp3):
-+ """
-+ This method is used to connect the Assimulo.Problem.jac to the Sundials
-+ Jacobian function.
-+ """
-+ cdef SUNMatrixContent_Dense Jacobian = <SUNMatrixContent_Dense>Jac.content
-+ cdef ProblemData pData = <ProblemData>problem_data
-+ cdef N.ndarray[realtype, ndim=2, mode='c'] jac #Used for return from the user function
-+ cdef realtype* col_i=Jacobian.cols[0]
-+ cdef N.ndarray y = pData.work_y
-+ cdef N.ndarray yd = pData.work_yd
-+ cdef int i,j, Neq = pData.dim
-+
-+ nv2arr_inplace(yv, y)
-+ nv2arr_inplace(yvdot, yd)
-+
-+ if pData.dimSens!=0: #SENSITIVITY
-+ p = realtype2arr(pData.p,pData.dimSens)
-+ try:
-+ if pData.sw != NULL:
-+ jac=(<object>pData.JAC)(c,t,y,yd,sw=<list>pData.sw,p=p) # call to the python residual function
-+ else:
-+ jac=(<object>pData.JAC)(c,t,y,yd,p=p)
-+
-+ for i in range(Neq):
-+ col_i = Jacobian.cols[i]
-+ for j in range(Neq):
-+ col_i[j] = jac[j,i]
-+ return IDADLS_SUCCESS
-+ except(N.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError):
-+ return IDADLS_JACFUNC_RECVR #Recoverable Error
-+ except:
-+ traceback.print_exc()
-+ return IDADLS_JACFUNC_UNRECVR
-+ else:
-+ try:
-+ if pData.sw != NULL:
-+ jac=(<object>pData.JAC)(c,t,y,yd,<list>pData.sw) # call to the python residual function
-+ else:
-+ jac=(<object>pData.JAC)(c,t,y,yd)
-+
-+ for i in range(Neq):
-+ col_i = Jacobian.cols[i]
-+ for j in range(Neq):
-+ col_i[j] = jac[j,i]
-+ return IDADLS_SUCCESS
-+ except(N.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError):
-+ return IDADLS_JACFUNC_RECVR #Recoverable Error
-+ except:
-+ traceback.print_exc()
-+ return IDADLS_JACFUNC_UNRECVR
-+ELSE:
-+ cdef int ida_jac(int Neq, realtype t, realtype c, N_Vector yv, N_Vector yvdot, N_Vector residual, DlsMat Jacobian,
- void* problem_data, N_Vector tmp1, N_Vector tmp2, N_Vector tmp3):
-- """
-- This method is used to connect the Assimulo.Problem.jac to the Sundials
-- Jacobian function.
-- """
-- cdef ProblemData pData = <ProblemData>problem_data
-- cdef N.ndarray[realtype, ndim=2, mode='c'] jac #Used for return from the user function
-- cdef realtype* col_i=DENSE_COL(Jacobian,0)
-- #(<ndarray>pData.y).data = <realtype*>((<N_VectorContent_Serial>yv.content).data)
-- #(<ndarray>pData.yd).data = <realtype*>((<N_VectorContent_Serial>yvdot.content).data)
-- cdef N.ndarray y = pData.work_y
-- cdef N.ndarray yd = pData.work_yd
-- #cdef N.ndarray y = nv2arr(yv)
-- #cdef N.ndarray yd = nv2arr(yvdot)
-- cdef int i,j
--
-- nv2arr_inplace(yv, y)
-- nv2arr_inplace(yvdot, yd)
--
-- if pData.dimSens!=0: #SENSITIVITY
-- p = realtype2arr(pData.p,pData.dimSens)
-- try:
-- if pData.sw != NULL:
-- jac=(<object>pData.JAC)(c,t,y,yd,sw=<list>pData.sw,p=p) # call to the python residual function
-- else:
-- jac=(<object>pData.JAC)(c,t,y,yd,p=p)
-+ """
-+ This method is used to connect the Assimulo.Problem.jac to the Sundials
-+ Jacobian function.
-+ """
-+ cdef ProblemData pData = <ProblemData>problem_data
-+ cdef N.ndarray[realtype, ndim=2, mode='c'] jac #Used for return from the user function
-+ cdef realtype* col_i=DENSE_COL(Jacobian,0)
-+ cdef N.ndarray y = pData.work_y
-+ cdef N.ndarray yd = pData.work_yd
-+ cdef int i,j
-+
-+ nv2arr_inplace(yv, y)
-+ nv2arr_inplace(yvdot, yd)
-+
-+ if pData.dimSens!=0: #SENSITIVITY
-+ p = realtype2arr(pData.p,pData.dimSens)
-+ try:
-+ if pData.sw != NULL:
-+ jac=(<object>pData.JAC)(c,t,y,yd,sw=<list>pData.sw,p=p) # call to the python residual function
-+ else:
-+ jac=(<object>pData.JAC)(c,t,y,yd,p=p)
-+
-+ for i in range(Neq):
-+ col_i = DENSE_COL(Jacobian, i)
-+ for j in range(Neq):
-+ col_i[j] = jac[j,i]
-+ return IDADLS_SUCCESS
-+ except(N.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError):
-+ return IDADLS_JACFUNC_RECVR #Recoverable Error
-+ except:
-+ traceback.print_exc()
-+ return IDADLS_JACFUNC_UNRECVR
-+ else:
-+ try:
-+ if pData.sw != NULL:
-+ jac=(<object>pData.JAC)(c,t,y,yd,<list>pData.sw) # call to the python residual function
-+ else:
-+ jac=(<object>pData.JAC)(c,t,y,yd)
-+
-+ for i in range(Neq):
-+ col_i = DENSE_COL(Jacobian, i)
-+ for j in range(Neq):
-+ col_i[j] = jac[j,i]
-+ return IDADLS_SUCCESS
-+ except(N.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError):
-+ return IDADLS_JACFUNC_RECVR #Recoverable Error
-+ except:
-+ traceback.print_exc()
-+ return IDADLS_JACFUNC_UNRECVR
-
-- for i in range(Neq):
-- col_i = DENSE_COL(Jacobian, i)
-- for j in range(Neq):
-- col_i[j] = jac[j,i]
-- return IDADLS_SUCCESS
-- except(N.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError):
-- return IDADLS_JACFUNC_RECVR #Recoverable Error
-- except:
-- traceback.print_exc()
-- return IDADLS_JACFUNC_UNRECVR
-- else:
-- try:
-- if pData.sw != NULL:
-- jac=(<object>pData.JAC)(c,t,y,yd,<list>pData.sw) # call to the python residual function
-- else:
-- jac=(<object>pData.JAC)(c,t,y,yd)
--
-- for i in range(Neq):
-- col_i = DENSE_COL(Jacobian, i)
-- for j in range(Neq):
-- col_i[j] = jac[j,i]
-- return IDADLS_SUCCESS
-- except(N.linalg.LinAlgError,ZeroDivisionError,AssimuloRecoverableError):
-- return IDADLS_JACFUNC_RECVR #Recoverable Error
-- except:
-- traceback.print_exc()
-- return IDADLS_JACFUNC_UNRECVR
--
-
- cdef int ida_root(realtype t, N_Vector yv, N_Vector yvdot, realtype *gout, void* problem_data):
- """
-@@ -479,12 +628,8 @@
- """
- cdef ProblemData pData = <ProblemData>problem_data
- cdef N.ndarray[realtype, ndim=1, mode='c'] root #Used for return from the user function
-- #(<ndarray>pData.y).data = <realtype*>((<N_VectorContent_Serial>yv.content).data)
-- #(<ndarray>pData.yd).data = <realtype*>((<N_VectorContent_Serial>yvdot.content).data)
- cdef N.ndarray y = pData.work_y
- cdef N.ndarray yd = pData.work_yd
-- #cdef N.ndarray y = nv2arr(yv)
-- #cdef N.ndarray yd = nv2arr(yvdot)
- cdef int i
-
- nv2arr_inplace(yv, y)
-@@ -553,30 +698,54 @@
- traceback.print_exc()
- return SPGMR_PSOLVE_FAIL_UNREC
-
-+IF SUNDIALS_VERSION >= (3,0,0):
-+ cdef int kin_jac(N_Vector xv, N_Vector fval, SUNMatrix Jac,
-+ void *problem_data, N_Vector tmp1, N_Vector tmp2):
-+ """
-+ This method is used to connect the assimulo.Problem.jac to the Sundials
-+ Jacobian function.
-+ """
-+ cdef SUNMatrixContent_Dense Jacobian = <SUNMatrixContent_Dense>Jac.content
-+ cdef ProblemDataEquationSolver pData = <ProblemDataEquationSolver>problem_data
-+ cdef realtype* col_i=Jacobian.cols[0]
-+ cdef N.ndarray x = nv2arr(xv)
-+ cdef int i,j, Neq = pData.dim
-+
-+ try:
-+ jac=(<object>pData.JAC)(x)
-
--cdef int kin_jac(int Neq, N_Vector xv, N_Vector fval, DlsMat Jacobian,
-- void *problem_data, N_Vector tmp1, N_Vector tmp2):
-- """
-- This method is used to connect the assimulo.Problem.jac to the Sundials
-- Jacobian function.
-- """
-- cdef ProblemDataEquationSolver pData = <ProblemDataEquationSolver>problem_data
-- cdef realtype* col_i=DENSE_COL(Jacobian,0)
-- cdef N.ndarray x = nv2arr(xv)
-- cdef int i,j
--
-- try:
-- jac=(<object>pData.JAC)(x)
-+ for i in range(Neq):
-+ col_i = Jacobian.cols[i]
-+ for j in range(Neq):
-+ col_i[j] = jac[j,i]
-
-- for i in range(Neq):
-- col_i = DENSE_COL(Jacobian, i)
-- for j in range(Neq):
-- col_i[j] = jac[j,i]
-+ return KINDLS_SUCCESS
-+ except:
-+ return KINDLS_JACFUNC_RECVR #Recoverable Error (See Sundials description)
-+ELSE:
-+ cdef int kin_jac(int Neq, N_Vector xv, N_Vector fval, DlsMat Jacobian,
-+ void *problem_data, N_Vector tmp1, N_Vector tmp2):
-+ """
-+ This method is used to connect the assimulo.Problem.jac to the Sundials
-+ Jacobian function.
-+ """
-+ cdef ProblemDataEquationSolver pData = <ProblemDataEquationSolver>problem_data
-+ cdef realtype* col_i=DENSE_COL(Jacobian,0)
-+ cdef N.ndarray x = nv2arr(xv)
-+ cdef int i,j
-+
-+ try:
-+ jac=(<object>pData.JAC)(x)
-
-- return KINDLS_SUCCESS
-- except:
-- return KINDLS_JACFUNC_RECVR #Recoverable Error (See Sundials description)
--
-+ for i in range(Neq):
-+ col_i = DENSE_COL(Jacobian, i)
-+ for j in range(Neq):
-+ col_i[j] = jac[j,i]
-+
-+ return KINDLS_SUCCESS
-+ except:
-+ return KINDLS_JACFUNC_RECVR #Recoverable Error (See Sundials description)
-+
- cdef int kin_jacv(N_Vector vv, N_Vector Jv, N_Vector vx, bint new_u,
- void *problem_data):
- cdef ProblemDataEquationSolver pData = <ProblemDataEquationSolver>problem_data
-Index: examples/cvode_with_jac.py
-===================================================================
---- assimulo/examples/cvode_with_jac.py (revision 844)
-+++ assimulo/examples/cvode_with_jac.py (revision 845)
-@@ -46,7 +46,6 @@
- def f(t,y):
- yd_0 = y[1]
- yd_1 = -9.82
-- #print y, yd_0, yd_1
- return N.array([yd_0,yd_1])
-
- #Defines the Jacobian
-Index: examples/__init__.py
-===================================================================
---- assimulo/examples/__init__.py (revision 844)
-+++ assimulo/examples/__init__.py (revision 845)
-@@ -24,6 +24,6 @@
- "mech_system_pendulum", "euler_vanderpol", "cvode_with_parameters_modified",
- "cvode_basic_backward","ida_basic_backward","dasp3_basic", "cvode_with_preconditioning",
- "kinsol_basic","kinsol_with_jac", "radau5dae_time_events", "kinsol_ors", "lsodar_bouncing_ball",
-- "cvode_with_parameters_fcn", "ida_with_user_defined_handle_result"]
-+ "cvode_with_parameters_fcn", "ida_with_user_defined_handle_result", "cvode_with_jac_sparse"]
-
-
-Index: examples/cvode_with_jac_sparse.py
-===================================================================
---- assimulo/examples/cvode_with_jac_sparse.py (nonexistent)
-+++ assimulo/examples/cvode_with_jac_sparse.py (revision 845)
-@@ -0,0 +1,102 @@
-+#!/usr/bin/env python
-+# -*- coding: utf-8 -*-
-+
-+# Copyright (C) 2010 Modelon AB
-+#
-+# This program is free software: you can redistribute it and/or modify
-+# it under the terms of the GNU Lesser General Public License as published by
-+# the Free Software Foundation, version 3 of the License.
-+#
-+# This program is distributed in the hope that it will be useful,
-+# but WITHOUT ANY WARRANTY; without even the implied warranty of
-+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-+# GNU Lesser General Public License for more details.
-+#
-+# You should have received a copy of the GNU Lesser General Public License
-+# along with this program. If not, see <http://www.gnu.org/licenses/>.
-+
-+import numpy as N
-+import scipy.sparse as SP
-+import pylab as P
-+import nose
-+from assimulo.solvers import CVode
-+from assimulo.problem import Explicit_Problem
-+
-+
-+def run_example(with_plots=True):
-+ r"""
-+ Example for demonstrating the use of a user supplied Jacobian (sparse).
-+ Note that this will only work if Assimulo has been configured with
-+ Sundials + SuperLU. Based on the SUNDIALS example cvRoberts_sps.c
-+
-+ ODE:
-+
-+ .. math::
-+
-+ \dot y_1 &= -0.04y_1 + 1e4 y_2 y_3 \\
-+ \dot y_2 &= - \dot y_1 - \dot y_3 \\
-+ \dot y_3 &= 3e7 y_2^2
-+
-+
-+ on return:
-+
-+ - :dfn:`exp_mod` problem instance
-+
-+ - :dfn:`exp_sim` solver instance
-+
-+ """
-+
-+ #Defines the rhs
-+ def f(t,y):
-+ yd_0 = -0.04*y[0] + 1e4*y[1]*y[2]
-+ yd_2 = 3e7*y[1]*y[1]
-+ yd_1 = -yd_0 - yd_2
-+ return N.array([yd_0,yd_1,yd_2])
-+
-+ #Defines the Jacobian
-+ def jac(t,y):
-+
-+ colptrs = [0,3,6,9]
-+ rowvals = [0, 1, 2, 0, 1, 2, 0, 1, 2]
-+ data = [-0.04, 0.04, 0.0, 1e4*y[2], -1e4*y[2]-6e7*y[1], 6e7*y[1], 1e4*y[1], -1e4*y[1], 0.0]
-+
-+ J = SP.csc_matrix((data, rowvals, colptrs))
-+ return J
-+
-+ #Defines an Assimulo explicit problem
-+ y0 = [1.0,0.0,0.0] #Initial conditions
-+
-+ exp_mod = Explicit_Problem(f,y0, name = 'Example using analytic (sparse) Jacobian')
-+
-+ exp_mod.jac = jac #Sets the Jacobian
-+ exp_mod.jac_nnz = 9
-+
-+
-+ exp_sim = CVode(exp_mod) #Create a CVode solver
-+
-+ #Set the parameters
-+ exp_sim.iter = 'Newton' #Default 'FixedPoint'
-+ exp_sim.discr = 'BDF' #Default 'Adams'
-+ exp_sim.atol = [1e-8,1e-14,1e-6] #Default 1e-6
-+ exp_sim.rtol = 1e-4 #Default 1e-6
-+ exp_sim.linear_solver = "sparse"
-+
-+ #Simulate
-+ t, y = exp_sim.simulate(0.4) #Simulate 0.4 seconds
-+
-+ #Basic tests
-+ nose.tools.assert_almost_equal(y[-1][0],0.9851,3)
-+
-+ #Plot
-+ if with_plots:
-+ P.plot(t,y[:,1],linestyle="dashed",marker="o") #Plot the solution
-+ P.xlabel('Time')
-+ P.ylabel('State')
-+ P.title(exp_mod.name)
-+ P.show()
-+
-+ return exp_mod, exp_sim
-+
-+
-+if __name__=='__main__':
-+ mod,sim = run_example()
-Index: tests/test_examples.py
-===================================================================
---- assimulo/tests/test_examples.py (revision 844)
-+++ assimulo/tests/test_examples.py (revision 845)
-@@ -22,7 +22,15 @@
-
- class Test_Examples:
-
-+
- @testattr(stddist = True)
-+ def test_cvode_with_jac_sparse(self):
-+ try:
-+ cvode_with_jac_sparse.run_example(with_plots=False)
-+ except AssimuloException:
-+ pass #Handle the case when SuperLU is not installed
-+
-+ @testattr(stddist = True)
- def test_ida_with_user_defined_handle_result(self):
- ida_with_user_defined_handle_result.run_example(with_plots=False)
-
diff --git a/sundials4.patch b/sundials4.patch
new file mode 100644
index 000000000000..05a302cadae8
--- /dev/null
+++ b/sundials4.patch
@@ -0,0 +1,26 @@
+Index: src/lib/sundials_includes.pxd
+===================================================================
+--- src/lib/sundials_includes.pxd (revision 875)
++++ src/lib/sundials_includes.pxd (working copy)
+@@ -237,7 +237,7 @@
+
+
+ cdef extern from "cvodes/cvodes.h":
+- void* CVodeCreate(int lmm, int iter)
++ void* CVodeCreate(int lmm)
+ ctypedef int (*CVRhsFn)(realtype t, N_Vector y, N_Vector ydot, void *f_data)
+ int CVodeInit(void *cvode_mem, CVRhsFn f, realtype t0, N_Vector y0)
+ int CVodeReInit(void *cvode_mem, realtype t0, N_Vector y0)
+Index: src/solvers/sundials.pyx
+===================================================================
+--- src/solvers/sundials.pyx (revision 875)
++++ src/solvers/sundials.pyx (working copy)
+@@ -1699,7 +1699,7 @@
+ if self.cvode_mem == NULL: #The solver is not initialized
+
+ #Create the solver
+- self.cvode_mem= SUNDIALS.CVodeCreate(CV_BDF if self.options["discr"] == "BDF" else CV_ADAMS, CV_NEWTON if self.options["iter"] == "Newton" else CV_FUNCTIONAL)
++ self.cvode_mem= SUNDIALS.CVodeCreate(CV_BDF if self.options["discr"] == "BDF" else CV_ADAMS)
+ if self.cvode_mem == NULL:
+ raise CVodeError(CV_MEM_FAIL)
+