n = 200 # number of increments
s_levels = np.linspace(0, 0.00125, 2)
s_levels.reshape(-1, 2)[:, 0] = 0.0
#s_levels.reshape(-1, 2)[:, 1] = -0.005
s_levels[0] = 0
s_history_1 = s_levels.flatten()
d_array = np.hstack([
np.linspace(s_history_1[i], s_history_1[i + 1], n)
for i in range(len(s_levels) - 1)
])
t_max = 1.0
t_arr = np.linspace(0, t_max, len(d_array))
print 't_arr ', t_arr
time_func = MFnLineArray(xdata=t_arr, ydata=d_array)
time_func.extrapolate = 'constant'
print 'time_func', time_func
time_func_2 = interp1d(t_arr, d_array)
print 'time_func_2', time_func_2
# ts.bc_list = [BCDof(var='u', dof=0, value=0.0), BCDof(var='f', dof=n_dofs - 1, time_function=loading_scenario.time_func)]
if False:
tloop.bc_list = [
BCSlice(slice=dots.mesh[0, :, 0, :], var='u', dims=[0, 1],
value=0),
BCSlice(slice=dots.mesh[25, -1, :, -1],
var='u',
dims=[1],
value=-50),
BCSlice(slice=dots.mesh[-1, :, -1, :],
