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)