def setup_sinusodial():
# sinusodial test signal:
s = State()
y, my, mdy = symbols("y, my, mdy")
s[my] = dda.neg(y)
s[y] = dda.int(mdy, dt, 0)
s[mdy] = dda.int(my, dt, 1)
s[diff_in] = y # diff_in = cos(t)
s[diff_out] = dda.neg(dda.diff(diff_in, dt, 0)) # 0 = sin(0)
s[int_out] = dda.neg(dda.int(diff_in, dt, +1)) # -1 = cos(0), but negated
return s
err = 0.5
ic_y = 1.0
ic_mdy = 0.0
s[my] = dda.neg(y)
s[y] = dda.int(lu, mdy, ld, dda.mult(err,my), dt, ic_y)
s[mdy] = dda.int(my, dt, ic_mdy) # my = ddy
s[lu] = dda.mult(1, dda.dead_upper(y, +1))
s[ld] = dda.mult(1, dda.dead_lower(y, -1))
# count zero crossings:
ys, yd, yi = symbols("ys, yd, yi")
s[ys] = dda.sign(y)
s[yd] = dda.max(0, dda.min(1, dda.diff(ys, dt, 0)))
s[yi] = dda.neg(dda.int(dda.mult(yd,1/dt), dt, 0))
# Frequenzverdopplung ohne linearem Offset
s[y2] = dda.mult(y,y)
s[y2ohne] = dda.diff(y2, dt, 0)
data = s.export(to="CppSolver").run(max_iterations= t_final / dt, rk_order=1).as_recarray()
xtime = np.arange(0, t_final, dt)
assert len(data) == len(xtime)
from matplotlib.pylab import *
ion(); clf()
#cols = data.dtype.names
cols = "y y2 y2ohne".split()
def setup_constant():
s = State()
#s[diff_in] = dda.const(1) # TODO: Such an expression does no more work!!
s[diff_out] = dda.diff(1, dt, 0)
s[int_diff] = dda.int(diff_out, dt, 1) # integrates over 0, should do it.
return s
def setup_polynomial():
s = State()
s[diff_in] = dda.mult(2,dda.int(dda.int(1, dt, 0), dt, 0)) # diff_in = 2*int(1) = t^2
s[diff_out] = dda.neg(dda.diff(diff_in, dt, 0)) # 2*t
s[int_diff] = dda.neg(dda.int(diff_out, dt, 0)) # t^2 but with increasing deviations due to dt
return s
alpha = 0.9
ic = 0
# test signal:
#s[diff_in] = dda.int(dda.int(1, dt, 0), dt, 0) # diff_in = t^2
# sinusodial test signal:
y, my, mdy = symbols("y, my, mdy")
s[my] = dda.neg(y)
s[y] = dda.int(mdy, dt, 0)
s[mdy] = dda.int(my, dt, 1)
s[diff_in] = y # diff_in = cos(t)
s[diff_out] = dda.diff(diff_in, dt, 0)
from dda.cpp_exporter import compile, run
compile(s.export(to="C"))
data = run(arguments={'max_iterations': t_final / dt, "rk_order": 4} )# return_recarray=True)
xtime = np.arange(0, t_final, dt)
assert len(data) == len(xtime)
from matplotlib.pylab import *
ion(); clf()
#cols = data.dtype.names
cols = "diff_in diff_out".split()