def sweep(self, p, pre=None, span=0.5, post=0.1, magnitude=1e-2, start=20, stop=10000):
#s, d = self.make_sweep(pre, span, post, magnitude, start, stop)
s = self.make_sweep(pre, span, post, magnitude, start, stop)
a = self.op_signal(samples=len(s), op=self.V.get("OP",[0.]))
a[:,0] += s
y = p(a)-p.o0
#return dk_lib.fft_convolve(d, y[:,0])
return dk_lib.fft_convolve(s, y[:,0], invert=True)
def spectrum_signal(self, p):
if 0:
s, d = self.make_sweep()
a = self.op_signal(samples=len(s), op=self.V["OP"])
a[:, 0] += s
a[:, 1] -= s
y = p(a) - p.o0
return dk_lib.fft_convolve(d, y)
else:
magnitude = 1e-3
a = self.op_signal(samples=64 * 1024, op=self.V["OP"])
a[0, 0] += magnitude
a[0, 1] -= magnitude
return (p(a) - p.o0) / magnitude
def spectrum_signal(self, p):
if 0:
s, d = self.make_sweep()
a = self.op_signal(samples=len(s), op=self.V["OP"])
a[:,0] += s
a[:,1] -= s
y = p(a)-p.o0
return dk_lib.fft_convolve(d, y)
else:
magnitude = 1e-3
a = self.op_signal(samples=64*1024, op=self.V["OP"])
a[0,0] += magnitude
a[0,1] -= magnitude
return (p(a)-p.o0) / magnitude
def sweep(self,
p,
pre=None,
span=0.5,
post=0.1,
magnitude=1e-2,
start=20,
stop=10000):
#s, d = self.make_sweep(pre, span, post, magnitude, start, stop)
s = self.make_sweep(pre, span, post, magnitude, start, stop)
a = self.op_signal(samples=len(s), op=self.V.get("OP", [0.]))
a[:, 0] += s
y = p(a) - p.o0
#return dk_lib.fft_convolve(d, y[:,0])
return dk_lib.fft_convolve(s, y[:, 0], invert=True)
