params_flow_int_ref = copy.deepcopy(params_flow_int)
N = int(params_flow_int_ref.nTimeSteps) * 1
# in general, this doesn't have to evenly spaced. Just in the right range.
x_dense=cpa_space.get_x_dense(nPts=1000)
# This needs to be evenly spaced.
interval = np.linspace(-3,3,x_dense.size)
Nvals = range(N)
Nvals=Nvals[-1:]
# x_dense = CpuGpuArray(x_dense)
v_dense = CpuGpuArray.empty_like(x_dense)
src = x_dense
transformed = CpuGpuArray.empty_like(src)
for i,N in enumerate(Nvals):
print 'i =',i
params_flow_int.nTimeSteps = float(N+1)
if i == 0:
cpa_space.calc_v(pts=src,out=v_dense)
# for j in range(1):
# if j % 100 == 0:
# print j
plt.figure()
params_flow_int_ref = copy.deepcopy(params_flow_int)
N = int(params_flow_int_ref.nTimeSteps) * 1
# in general, this doesn't have to evenly spaced. Just in the right range.
x_dense = cpa_space.get_x_dense(nPts=1000)
# This needs to be evenly spaced.
interval = np.linspace(-3, 3, x_dense.size)
Nvals = range(N)
Nvals = Nvals[-1:]
# x_dense = CpuGpuArray(x_dense)
v_dense = CpuGpuArray.empty_like(x_dense)
src = x_dense
transformed = CpuGpuArray.empty_like(src)
for i, N in enumerate(Nvals):
print 'i =', i
params_flow_int.nTimeSteps = float(N + 1)
if i == 0:
cpa_space.calc_v(pts=src, out=v_dense)
# for j in range(1):
# if j % 100 == 0:
# print j
# cpa_space.calc_T(pat,pts = src, mysign=1,out=transformed,
# **params_flow_int)
print 'nPts =', len(src)
M = 1
