def correct(self, pos):
delta1 = fitpack.bisplev(pos[1], pos[0], [self.xSplineKnotsX,
self.xSplineKnotsY,
self.xSplineCoeff,
self.splineOrder,
self.splineOrder],
dx=0, dy=0)
delta0 = fitpack.bisplev(pos[1], pos[0], [self.ySplineKnotsX,
self.ySplineKnotsY,
self.ySplineCoeff,
self.splineOrder,
self.splineOrder],
dx=0, dy=0)
return delta0 + pos[0], delta1 + pos[1]
def correct(self, pos):
delta1 = fitpack.bisplev(pos[1], pos[0], [self.xSplineKnotsX,
self.xSplineKnotsY,
self.xSplineCoeff,
self.splineOrder,
self.splineOrder],
dx=0, dy=0)
delta0 = fitpack.bisplev(pos[1], pos[0], [self.ySplineKnotsX,
self.ySplineKnotsY,
self.ySplineCoeff,
self.splineOrder,
self.splineOrder],
dx=0, dy=0)
return delta0 + pos[0], delta1 + pos[1]
def splineFuncY(self, x, y):
"""
calculates the displacement matrix using fitpack for the Y
direction
@param x: points in the x direction
@type x: ndarray
@param y: points in the y direction
@type y: ndarray
@return: displacement matrix for the Y direction
@rtype: ndarray
"""
if x.ndim == 2:
if abs(x[1:, :] - x[:-1, :] - numpy.zeros((x.shape[0] - 1, x.shape[1]))).max() < 1e-6:
x = x[0]
y = y[:, 0]
elif abs(x[:, 1:] - x[:, :-1] - numpy.zeros((x.shape[0], x.shape[1] - 1))).max() < 1e-6:
x = x[:, 0]
y = y[0]
yDispArray = fitpack.bisplev(
x, y, [self.ySplineKnotsX,
self.ySplineKnotsY,
self.ySplineCoeff,
self.splineOrder,
self.splineOrder ],
dx=0, dy=0).transpose()
return yDispArray
def test_ilp64_bisplrep(self):
check_free_memory(28000) # VM size, doesn't actually use the pages
x = np.linspace(0, 1, 400)
y = np.linspace(0, 1, 400)
x, y = np.meshgrid(x, y)
z = np.zeros_like(x)
tck = bisplrep(x, y, z, kx=3, ky=3, s=0)
assert_allclose(bisplev(0.5, 0.5, tck), 0.0)
def spline2array(self, timing=False):
"""
Calculates the displacement matrix using fitpack
bisplev(x, y, tck, dx = 0, dy = 0)
:param timing: profile the calculation or not
:type timing: bool
:return: xDispArray, yDispArray
:rtype: 2-tuple of ndarray
Evaluate a bivariate B-spline and its derivatives. Return a
rank-2 array of spline function values (or spline derivative
values) at points given by the cross-product of the rank-1
arrays x and y. In special cases, return an array or just a
float if either x or y or both are floats.
"""
if self.xDispArray is None:
x_1d_array = numpy.arange(self.xmin, self.xmax + 1)
y_1d_array = numpy.arange(self.ymin, self.ymax + 1)
startTime = time.time()
self.xDispArray = fitpack.bisplev(
x_1d_array,
y_1d_array, [
self.xSplineKnotsX, self.xSplineKnotsY, self.xSplineCoeff,
self.splineOrder, self.splineOrder
],
dx=0,
dy=0).transpose()
intermediateTime = time.time()
self.yDispArray = fitpack.bisplev(
x_1d_array,
y_1d_array, [
self.ySplineKnotsX, self.ySplineKnotsY, self.ySplineCoeff,
self.splineOrder, self.splineOrder
],
dx=0,
dy=0).transpose()
if timing:
logger.info(
"Timing for: X-Displacement spline evaluation: %.3f sec,"
" Y-Displacement Spline evaluation: %.3f sec." %
((intermediateTime - startTime),
(time.time() - intermediateTime)))
return self.xDispArray, self.yDispArray
def get_derivative(self, x, dx=None):
"""Evaluate derivative at x"""
if dx is None:
dx = [0, 0]
knots = self.rbs.get_knots()
coeffs = self.rbs.get_coeffs()
tck = [knots[0], knots[1], coeffs, self.kx0, self.kx1]
return fitpack.bisplev(x[0], x[1], tck, dx[0], dx[1])
def spline2array(self, timing=False):
"""
Calculates the displacement matrix using fitpack
bisplev(x, y, tck, dx = 0, dy = 0)
:param timing: profile the calculation or not
:type timing: bool
:return: Nothing !
:rtype: float or ndarray
Evaluate a bivariate B-spline and its derivatives. Return a
rank-2 array of spline function values (or spline derivative
values) at points given by the cross-product of the rank-1
arrays x and y. In special cases, return an array or just a
float if either x or y or both are floats.
"""
if self.xDispArray is None:
x_1d_array = numpy.arange(self.xmin, self.xmax + 1)
y_1d_array = numpy.arange(self.ymin, self.ymax + 1)
startTime = time.time()
self.xDispArray = fitpack.bisplev(
x_1d_array,
y_1d_array,
[self.xSplineKnotsX, self.xSplineKnotsY, self.xSplineCoeff, self.splineOrder, self.splineOrder],
dx=0,
dy=0,
).transpose()
intermediateTime = time.time()
self.yDispArray = fitpack.bisplev(
x_1d_array,
y_1d_array,
[self.ySplineKnotsX, self.ySplineKnotsY, self.ySplineCoeff, self.splineOrder, self.splineOrder],
dx=0,
dy=0,
).transpose()
if timing:
logger.info(
"Timing for: X-Displacement spline evaluation: %.3f sec,"
" Y-Displacement Spline evaluation: %.3f sec."
% ((intermediateTime - startTime), (time.time() - intermediateTime))
)
def check_5(self,f=f2,kx=3,ky=3,xb=0,xe=2*pi,yb=0,ye=2*pi,Nx=20,Ny=20,s=0):
x = xb+(xe-xb)*arange(Nx+1,dtype=float)/float(Nx)
y = yb+(ye-yb)*arange(Ny+1,dtype=float)/float(Ny)
xy = makepairs(x,y)
tck = bisplrep(xy[0],xy[1],f(xy[0],xy[1]),s=s,kx=kx,ky=ky)
tt = [tck[0][kx:-kx],tck[1][ky:-ky]]
t2 = makepairs(tt[0],tt[1])
v1 = bisplev(tt[0],tt[1],tck)
v2 = f2(t2[0],t2[1])
v2.shape = len(tt[0]),len(tt[1])
err = norm2(ravel(v1-v2))
assert_(err < 1e-2, err)
put(err)
def check_5(self,f=f2,kx=3,ky=3,xb=0,xe=2*pi,yb=0,ye=2*pi,Nx=20,Ny=20,s=0):
x=xb+(xe-xb)*arange(Nx+1,dtype=float)/float(Nx)
y=yb+(ye-yb)*arange(Ny+1,dtype=float)/float(Ny)
xy=makepairs(x,y)
tck=bisplrep(xy[0],xy[1],f(xy[0],xy[1]),s=s,kx=kx,ky=ky)
tt=[tck[0][kx:-kx],tck[1][ky:-ky]]
t2=makepairs(tt[0],tt[1])
v1=bisplev(tt[0],tt[1],tck)
v2=f2(t2[0],t2[1])
v2.shape=len(tt[0]),len(tt[1])
err = norm2(ravel(v1-v2))
assert_(err < 1e-2, err)
put(err)
def splineFuncY(self, x, y, list_of_points=False):
"""
calculates the displacement matrix using fitpack for the Y
direction
:param x: points in the x direction
:type x: ndarray
:param y: points in the y direction
:type y: ndarray
:param list_of_points: if true, consider the zip(x,y) instead of the of the square array
:return: displacement matrix for the Y direction
:rtype: ndarray
"""
if x.ndim == 2:
if abs(x[1:, :] - x[:-1, :] -
numpy.zeros((x.shape[0] - 1, x.shape[1]))).max() < 1e-6:
x = x[0]
y = y[:, 0]
elif abs(x[:, 1:] - x[:, :-1] -
numpy.zeros((x.shape[0], x.shape[1] - 1))).max() < 1e-6:
x = x[:, 0]
y = y[0]
if list_of_points and x.ndim == 1 and len(x) == len(y):
size = len(x)
if size > 1:
x_order = x.argsort()
y_order = y.argsort()
x = x[x_order]
y = y[y_order]
x_unordered = numpy.zeros(size, dtype=numpy.int32)
y_unordered = numpy.zeros(size, dtype=numpy.int32)
x_unordered[x_order] = numpy.arange(size)
y_unordered[y_order] = numpy.arange(size)
y_disp_array = fitpack.bisplev(
x,
y, [
self.ySplineKnotsX, self.ySplineKnotsY, self.ySplineCoeff,
self.splineOrder, self.splineOrder
],
dx=0,
dy=0)
if list_of_points and x.ndim == 1:
if size > 1:
return y_disp_array[x_unordered, y_unordered]
else:
return numpy.array([y_disp_array])
else:
return y_disp_array.T
def test_rerun_lwrk2_too_small(self):
# in this setting, lwrk2 is too small in the default run. Here we
# check for equality with the bisplrep/bisplev output because there,
# an automatic re-run of the spline representation is done if ier>10.
x = np.linspace(-2, 2, 80)
y = np.linspace(-2, 2, 80)
z = x + y
xi = np.linspace(-1, 1, 100)
yi = np.linspace(-2, 2, 100)
tck = bisplrep(x, y, z)
res1 = bisplev(xi, yi, tck)
interp_ = SmoothBivariateSpline(x, y, z)
res2 = interp_(xi, yi)
assert_almost_equal(res1, res2)
def test_rerun_lwrk2_too_small(self):
# in this setting, lwrk2 is too small in the default run. Here we
# check for equality with the bisplrep/bisplev output because there,
# an automatic re-run of the spline representation is done if ier>10.
x = np.linspace(-2, 2, 80)
y = np.linspace(-2, 2, 80)
z = x + y
xi = np.linspace(-1, 1, 100)
yi = np.linspace(-2, 2, 100)
tck = bisplrep(x, y, z)
res1 = bisplev(xi, yi, tck)
interp_ = SmoothBivariateSpline(x, y, z)
res2 = interp_(xi, yi)
assert_almost_equal(res1, res2)
def test_bispev(self):
x_1d_array = numpy.arange(self.spline.xmin, self.spline.xmax + 1)
y_1d_array = numpy.arange(self.spline.ymin, self.spline.ymax + 1)
t0 = time.time()
dx_ref = fitpack.bisplev(
x_1d_array,
y_1d_array,
[
self.spline.xSplineKnotsX,
self.spline.xSplineKnotsY,
self.spline.xSplineCoeff,
self.spline.splineOrder,
self.spline.splineOrder,
],
dx=0,
dy=0,
)
t1 = time.time()
logger.debug(self.spline.xSplineKnotsX.dtype)
logger.debug(self.spline.xSplineKnotsY.dtype)
logger.debug(self.spline.xSplineCoeff.dtype)
dx_loc = _bispev.bisplev(
x_1d_array,
y_1d_array,
[
self.spline.xSplineKnotsX,
self.spline.xSplineKnotsY,
self.spline.xSplineCoeff,
self.spline.splineOrder,
self.spline.splineOrder,
],
)
t2 = time.time()
logger.debug("Scipy timings: %.3fs\t cython timings: %.3fs" % (t1 - t0, t2 - t1))
logger.debug("%s, %s" % (dx_ref.shape, dx_loc.shape))
logger.debug(dx_ref)
logger.debug(dx_loc)
logger.debug("delta = %s" % abs(dx_loc - dx_ref).max())
if logger.getEffectiveLevel() == logging.DEBUG:
fig = pylab.figure()
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
ax1.imshow(dx_ref)
ax2.imshow(dx_loc)
fig.show()
six.moves.input()
self.assert_(abs(dx_loc - dx_ref).max() < 2e-5, "Result are similar")
def splineFuncX(self, x, y, list_of_points=False):
"""
Calculates the displacement matrix using fitpack for the X
direction on the given grid.
@param x: points of the grid in the x direction
@type x: ndarray
@param y: points of the grid in the y direction
@type y: ndarray
@param list_of_points: if true, consider the zip(x,y) instead of the of the square array
@return: displacement matrix for the X direction
@rtype: ndarray
"""
if x.ndim == 2:
if abs(x[1:, :] - x[:-1, :] -
numpy.zeros((x.shape[0] - 1, x.shape[1]))).max() < 1e-6:
x = x[0]
y = y[:, 0]
elif abs(x[:, 1:] - x[:, :-1] -
numpy.zeros((x.shape[0], x.shape[1] - 1))).max() < 1e-6:
x = x[:, 0]
y = y[0]
if list_of_points and x.ndim == 1 and len(x) == len(y):
lx = ly = len(x)
x_order = x.argsort()
y_order = y.argsort()
x = x[x_order]
y = y[y_order]
x_unordered = numpy.zeros(lx, dtype=int)
y_unordered = numpy.zeros(ly, dtype=int)
x_unordered[x_order] = numpy.arange(lx)
y_unordered[y_order] = numpy.arange(ly)
xDispArray = fitpack.bisplev(
x,
y, [
self.xSplineKnotsX, self.xSplineKnotsY, self.xSplineCoeff,
self.splineOrder, self.splineOrder
],
dx=0,
dy=0)
if list_of_points and x.ndim == 1:
return xDispArray[x_unordered, y_unordered]
else:
return xDispArray.T
def splineFuncY(self, x, y, list_of_points=False):
"""
calculates the displacement matrix using fitpack for the Y
direction
@param x: points in the x direction
@type x: ndarray
@param y: points in the y direction
@type y: ndarray
@param list_of_points: if true, consider the zip(x,y) instead of the of the square array
@return: displacement matrix for the Y direction
@rtype: ndarray
"""
if x.ndim == 2:
if abs(x[1:, :] - x[:-1, :] - numpy.zeros((x.shape[0] - 1, x.shape[1]))).max() < 1e-6:
x = x[0]
y = y[:, 0]
elif abs(x[:, 1:] - x[:, :-1] - numpy.zeros((x.shape[0], x.shape[1] - 1))).max() < 1e-6:
x = x[:, 0]
y = y[0]
if list_of_points and x.ndim == 1 and len(x) == len(y):
lx = ly = len(x)
x_order = x.argsort()
y_order = y.argsort()
x = x[x_order]
y = y[y_order]
x_unordered = numpy.zeros(lx, dtype=int)
y_unordered = numpy.zeros(ly, dtype=int)
x_unordered[x_order] = numpy.arange(lx)
y_unordered[y_order] = numpy.arange(ly)
yDispArray = fitpack.bisplev(
x, y, [self.ySplineKnotsX,
self.ySplineKnotsY,
self.ySplineCoeff,
self.splineOrder,
self.splineOrder ],
dx=0, dy=0)
if list_of_points and x.ndim == 1:
return yDispArray[x_unordered, y_unordered]
else:
return yDispArray.T
def test_bispev(self):
x_1d_array = numpy.arange(self.spline.xmin, self.spline.xmax + 1)
y_1d_array = numpy.arange(self.spline.ymin, self.spline.ymax + 1)
t0 = time.time()
dx_ref = fitpack.bisplev(
x_1d_array,
y_1d_array, [
self.spline.xSplineKnotsX, self.spline.xSplineKnotsY,
self.spline.xSplineCoeff, self.spline.splineOrder,
self.spline.splineOrder
],
dx=0,
dy=0)
t1 = time.time()
logger.debug(self.spline.xSplineKnotsX.dtype)
logger.debug(self.spline.xSplineKnotsY.dtype)
logger.debug(self.spline.xSplineCoeff.dtype)
dx_loc = _bispev.bisplev(
x_1d_array,
y_1d_array,
[
self.spline.xSplineKnotsX, self.spline.xSplineKnotsY,
self.spline.xSplineCoeff, self.spline.splineOrder,
self.spline.splineOrder
],
)
t2 = time.time()
logger.debug("Scipy timings: %.3fs\t cython timings: %.3fs", t1 - t0,
t2 - t1)
logger.debug("%s, %s", dx_ref.shape, dx_loc.shape)
logger.debug(dx_ref)
logger.debug(dx_loc)
logger.debug("delta = %s", abs(dx_loc - dx_ref).max())
if logger.getEffectiveLevel() == logging.DEBUG:
fig = pylab.figure()
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
ax1.imshow(dx_ref)
ax2.imshow(dx_loc)
fig.show()
six.moves.input()
self.assertTrue(
abs(dx_loc - dx_ref).max() < 2e-5, "Result are similar")
def make_diff_func(self) :
''' everytime parameters are changed, the diffusion
function must be REMADE or it's data adapted !!
'''
if (self.difftype == 'MC_2D4pint_ext1D'
or self.difftype == 'MC_2D4pint_func'):
from GridUtils import GridFunc2D
self.diff11 = GridFunc2D([self.param[0],self.param[1]],N.asarray(self.param[2]),False)
self.diff12 = GridFunc2D([self.param[0],self.param[1]],N.asarray(self.param[3]),False)
self.diff21 = GridFunc2D([self.param[0],self.param[1]],N.asarray(self.param[4]),False)
self.diff22 = GridFunc2D([self.param[0],self.param[1]],N.asarray(self.param[5]),False)
self.diff11_u = None;self.diff12_u = None;self.diff21_u = None;self.diff22_u = None;
self.diff11_v = None;self.diff12_v = None;self.diff21_v = None;self.diff22_v = None;
#grideval means a ugrid and vgrid is given, the grid made by this is evaluated
#this is used in the plotting function
self.diff11_grideval = GridFunc2D([self.param[0],self.param[1]],N.asarray(self.param[2]),True)
self.diff12_grideval = GridFunc2D([self.param[0],self.param[1]],N.asarray(self.param[3]),True)
self.diff21_grideval = GridFunc2D([self.param[0],self.param[1]],N.asarray(self.param[4]),True)
self.diff22_grideval = GridFunc2D([self.param[0],self.param[1]],N.asarray(self.param[5]),True)
elif self.difftype == 'MC_bspline_ext1D' or \
self.difftype == 'MC_bspline1storder_ext1D' :
self.arrayaware = False
weight = ones(self.nrparam)
# standard dev on diffusion = 0.2, inverse is 5
weight[:] =5 * weight[:]
#create an interp object
from scipy.interpolate.fitpack import bisplrep,bisplev
#param[0] should be list of u values
#param[1] should be list of v values
#param[2] should be list of D(u,v) values
#create the data of the bicubic bspline:
# no smoother so s = 0
if self.difftype == 'MC_bspline1storder_ext1D' :
self.splinedataD11 = bisplrep(self.param[0], self.param[1],
self.param[2], w= weight, s=None,
kx = 1, ky=1, full_output = 1, quiet = 0)
else :
#nknots = 11
#tx = range(nknots)
#tx = [self.umin + x/(1.*(nknots-1)) * (self.umax-self.umin) for x in tx]
#ty = range(nknots)
#ty = [self.vmin + x/(1.*(nknots-1)) * (self.vmax-self.vmin) for x in ty]
#tx = tx[0]*3 + tx + tx[-1]*3
#tx[:1] = [tx[0]]*4 ; tx[-1:] = [tx[-1]]*4
#ty[:1] = [ty[0]]*4 ; ty[-1:] = [ty[-1]]*4
#print 'tx,ty',tx, ty
self.splinedataD11 = bisplrep(self.param[0], self.param[1],
self.param[2], w= weight, s=2100,
kx = 3, ky=3, full_output = 1, quiet = 0)
#print 'D11 output :', self.splinedataD11[1:]
if self.splinedataD11[2] > 0 :
print 'ERROR in spline interpolation'
sys.exit()
self.splinedataD11 = self.splinedataD11[0]
self.diff11 = lambda u,v: bisplev([u], [v], self.splinedataD11, \
dx = 0, dy=0)
self.diff11_u = lambda u,v: bisplev([u], [v] , self.splinedataD11,\
dx = 1, dy=0)
self.diff11_v = lambda u,v: bisplev([u], [v] , self.splinedataD11,\
dx = 0, dy=1)
self.diff11_grideval = lambda u,v: bisplev(u, v, self.splinedataD11, \
dx = 0, dy=0)
self.diff11_u_grideval = lambda u,v: bisplev(u, v , self.splinedataD11,\
dx = 1, dy=0)
self.diff11_v_grideval = lambda u,v: bisplev(u, v , self.splinedataD11,\
dx = 0, dy=1)
if self.difftype == 'MC_bspline1storder_ext1D' :
self.splinedataD12 = bisplrep(self.param[0], self.param[1],
self.param[3], w= weight, s=None,
kx = 1, ky=1, full_output = 1, quiet = 1)
else :
self.splinedataD12 = bisplrep(self.param[0], self.param[1],
self.param[3], w= weight, s=None,
kx = 3, ky=3, full_output = 1, quiet = 0)
#print 'D12 output :', self.splinedataD12[1:]
if self.splinedataD12[2] > 0 :
print 'ERROR in spline interpolation'
sys.exit()
self.splinedataD12 = self.splinedataD12[0]
self.diff12 = lambda u,v: bisplev([u], [v], self.splinedataD12, \
dx = 0, dy=0)
self.diff12_u = lambda u,v: bisplev([u], [v] , self.splinedataD12,\
dx = 1, dy=0)
self.diff12_v = lambda u,v: bisplev([u], [v] , self.splinedataD12,\
dx = 0, dy=1)
self.diff12_grideval = lambda u,v: bisplev(u, v, self.splinedataD12, \
dx = 0, dy=0)
self.diff12_u_grideval = lambda u,v: bisplev(u, v , self.splinedataD12,\
dx = 1, dy=0)
self.diff12_v_grideval = lambda u,v: bisplev(u, v , self.splinedataD12,\
dx = 0, dy=1)
if self.difftype == 'MC_bspline1storder_ext1D' :
self.splinedataD21 = bisplrep(self.param[0], self.param[1],
self.param[4], w= weight, s=None,
kx = 1, ky=1, full_output = 1, quiet = 1)
else :
self.splinedataD21 = bisplrep(self.param[0], self.param[1],
self.param[4], w= weight, s=None,
kx = 3, ky=3, full_output = 1, quiet = 0)
#print 'D21 output :', self.splinedataD21[1:]
if self.splinedataD21[2] > 0 :
print 'ERROR in spline interpolation'
sys.exit()
self.splinedataD21 = self.splinedataD21[0]
self.diff21 = lambda u,v: bisplev([u], [v], self.splinedataD21, \
dx = 0, dy=0)
self.diff21_u = lambda u,v: bisplev([u], [v] , self.splinedataD21,\
dx = 1, dy=0)
self.diff21_v = lambda u,v: bisplev([u], [v] , self.splinedataD21,\
dx = 0, dy=1)
self.diff21_grideval = lambda u,v: bisplev(u, v, self.splinedataD21, \
dx = 0, dy=0)
self.diff21_u_grideval = lambda u,v: bisplev(u, v , self.splinedataD21,\
dx = 1, dy=0)
self.diff21_v_grideval = lambda u,v: bisplev(u, v , self.splinedataD21,\
dx = 0, dy=1)
if self.difftype == 'MC_bspline1storder_ext1D' :
self.splinedataD22 = bisplrep(self.param[0], self.param[1],
self.param[5], w= weight, s=None,
kx = 1, ky=1, full_output = 1, quiet = 1)
else :
self.splinedataD22 = bisplrep(self.param[0], self.param[1],
self.param[5], w= weight, s=55000.,
kx = 3, ky=3, full_output = 1, quiet = 0)
#print 'D22 output :', self.splinedataD22[1:]
if self.splinedataD22[2] > 0 :
print 'ERROR in spline interpolation'
sys.exit()
self.splinedataD22 = self.splinedataD22[0]
self.diff22 = lambda u,v: bisplev([u], [v], self.splinedataD22, \
dx = 0, dy=0)
self.diff22_u = lambda u,v: bisplev([u], [v] , self.splinedataD22,\
dx = 1, dy=0)
self.diff22_v = lambda u,v: bisplev([u], [v] , self.splinedataD22,\
dx = 0, dy=1)
self.diff22_grideval = lambda u,v: bisplev(u, v, self.splinedataD22, \
dx = 0, dy=0)
self.diff22_u_grideval = lambda u,v: bisplev(u, v , self.splinedataD22,\
dx = 1, dy=0)
self.diff22_v_grideval = lambda u,v: bisplev(u, v , self.splinedataD22,\
dx = 0, dy=1)
elif self.difftype == 'MC_interp2d_ext1D':
from scipy.interpolate.interpolate import interp2d
self.diff11 = interp2d(self.param[0],self.param[1],self.param[2])
self.diff12 = interp2d(self.param[0],self.param[1],self.param[3])
self.diff21 = interp2d(self.param[0],self.param[1],self.param[4])
self.diff22 = interp2d(self.param[0],self.param[1],self.param[5])
print 'diff11 test ' , self.diff11(1.5)
print ' *** DO NOT USE THIS INTERPOLATION, it performs badly ***'
sys.exit()
elif self.difftype == 'function' :
#self.diffij is defined when reading the init file for scalars,
pass
else:
print ('Unknown diffusion type given', self.difftype)
sys.exit()
#store the functions in a list:
self.diff = [[self.diff11 , self.diff12],[self.diff21 , self.diff22]]
self.diff_u = [[self.diff11_u , self.diff12_u],[self.diff21_u , self.diff22_u]]
self.diff_v = [[self.diff11_v , self.diff12_v],[self.diff21_v , self.diff22_v]]
#changed parameters are taken into account, so diffusion is now correct
self.modified = False
