def test_cache_argument(self):
x, y, z = np.arange(16), np.arange(16, 32), np.arange(32, 48)
bisplrep_cache = {
't': array([], float),
'wrk': array([], float),
'iwrk': array([], intc)
}
bisplrep(x, y, z, task=0, cache=bisplrep_cache)
bisplrep(x, y, z, task=1, cache=bisplrep_cache)
def test_regression_1310(self):
# Regression test for gh-1310
data = np.load(data_file("bug-1310.npz"))["data"]
# Shouldn't crash -- the input data triggers work array sizes
# that caused previously some data to not be aligned on
# sizeof(double) boundaries in memory, which made the Fortran
# code to crash when compiled with -O3
bisplrep(data[:, 0], data[:, 1], data[:, 2], kx=3, ky=3, s=0, full_output=True)
def test_regression_1310(self):
# Regression test for gh-1310
data = np.load(data_file('bug-1310.npz'))['data']
# Shouldn't crash -- the input data triggers work array sizes
# that caused previously some data to not be aligned on
# sizeof(double) boundaries in memory, which made the Fortran
# code to crash when compiled with -O3
bisplrep(data[:,0], data[:,1], data[:,2], kx=3, ky=3, s=0,
full_output=True)
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 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 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_task_argument(self):
x, y, z = np.arange(16), np.arange(16, 32), np.arange(32, 48)
bisplrep(x, y, z, task=0)
with pytest.raises(ValueError):
bisplrep(x, y, z, task=1)
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