def _fft_convolve(h, xd):
n = len(h) + len(xd) - 1
np = dk_lib.pow2roundup(n)
if len(xd.shape) == 2 and len(h.shape) == 1:
h = h.reshape((len(h), 1))
s = numpy.fft.irfft(numpy.fft.rfft(h, np, axis=0) * numpy.fft.rfft(xd, np, axis=0), np, axis=0)
return s[:n]
def plot_spectrum(self, p, plot_variable):
varlist = []
if plot_variable:
if plot_variable not in p.pot_list:
raise ArgumentError("variable %s not found" % plot_variable)
##hack
for k, t in self.V.items():
if isinstance(k, P):
if not isinstance(t, dict):
t = dict(value=t)
var = t.get('var')
if var is None:
var = str(k) + 'v'
if var == plot_variable:
break
loga = t.get('a', 0)
inv = t.get('inv', 0)
for i in range(5):
pot = i / 4
lbl = "%s" % pot
if inv:
pot = 1 - pot
if loga:
pot = (math.exp(loga * pot) - 1) / (math.exp(loga) - 1)
varlist.append((plot_variable, pot, lbl))
else:
varlist.append((None, None, p.out_labels))
n = None
cut = None
def spec(y):
s = 20 * np.log10(abs(np.fft.fft(y, n, axis=0)[cut]))
return np.where(s > -80, s, np.nan)
labels = []
for var, val, lbl in varlist:
if var is not None:
p.set_variable(var, val)
y = self.spectrum_signal(p, magnitude=1e-4)
if n is None:
n = dk_lib.pow2roundup(len(y))
cut = slice(n * 20 // int(self.FS), n * 10000 // int(self.FS))
w = fftfreq(n, 1.0 / self.FS)[cut]
pl.semilogx(w, spec(y), label=plot_variable)
pl.xlabel('Frequency')
pl.ylabel('Magnitude ')
if plot_variable:
pl.title(plot_variable)
if isinstance(lbl, basestring):
labels.append(lbl)
else:
labels.extend(lbl)
ax = pl.gca()
ax.grid()
ax.yaxis.set_major_formatter(pl.FormatStrFormatter('%d dB'))
ax.xaxis.set_major_formatter(pl.FormatStrFormatter('%d Hz'))
self.finish_plot(labels, loc='upper left')
def plot_spectrum(self, p, plot_variable):
y = self.spectrum_signal(p)
n = dk_lib.pow2roundup(len(y))
cut = slice(n*20.0/self.FS, n*10000.0/self.FS)
w = fftfreq(n,1.0/self.FS)[cut]
def spec(y):
s = 20*np.log10(abs(np.fft.fft(y,n,axis=0)[cut]))
return np.where(s > -80, s, np.nan)
pl.semilogx(w, spec(y))
self.finish_plot(p.out_labels)
def _fft_convolve(h, xd):
n = len(h) + len(xd) - 1
npow = dk_lib.pow2roundup(n)
if len(xd.shape) == 2 and len(h.shape) == 1:
h = h.reshape((len(h), 1))
s = np.fft.irfft(np.fft.rfft(h, npow, axis=0) *
np.fft.rfft(xd, npow, axis=0),
npow,
axis=0)
return s[:n]
def make_sweep(self, pre=None, span=0.5, post=0.1, magnitude=1e-2, start=20, stop=10000):
smpl = lambda tm: int(round(tm*self.FS))
if pre is None:
pre = span/2
s, d = dk_lib.genlogsweep(
start, stop, self.FS, smpl(pre), smpl(span), smpl(post))
s *= magnitude
n = dk_lib.pow2roundup(len(s))
#d /= np.mean(abs(np.fft.fft(dk_lib.fft_convolve(d, s), n))[n*start/self.FS:n*stop/self.FS])
#return s, d
return s
def plot_spectrum(self, p, plot_variable):
y = self.spectrum_signal(p)
n = dk_lib.pow2roundup(len(y))
cut = slice(n * 20.0 / self.FS, n * 10000.0 / self.FS)
w = fftfreq(n, 1.0 / self.FS)[cut]
def spec(y):
s = 20 * np.log10(abs(np.fft.fft(y, n, axis=0)[cut]))
return np.where(s > -80, s, np.nan)
pl.semilogx(w, spec(y))
self.finish_plot(p.out_labels)
def plot_spectrum(self, p, plot_variable):
varlist = []
if plot_variable:
if plot_variable not in p.pot_list:
raise ArgumentError("variable %s not found" % plot_variable)
##hack
for k, t in self.V.items():
if isinstance(k, P):
if not isinstance(t, dict):
t = dict(value=t)
var = t.get('var')
if var is None:
var = str(k)+'v'
if var == plot_variable:
break
loga = t.get('a', 0)
inv = t.get('inv', 0)
for i in range(5):
pot = i/4
lbl = "%s" % pot
if inv:
pot = 1 - pot
if loga:
pot = (math.exp(loga * pot) - 1) / (math.exp(loga) - 1)
varlist.append((plot_variable, pot, lbl))
else:
varlist.append((None, None, p.out_labels))
n = None
cut = None
def spec(y):
s = 20*np.log10(abs(np.fft.fft(y,n,axis=0)[cut]))
return np.where(s > -80, s, np.nan)
labels = []
for var, val, lbl in varlist:
if var is not None:
p.set_variable(var, val)
y = self.spectrum_signal(p, magnitude=1e-4)
if n is None:
n = dk_lib.pow2roundup(len(y))
cut = slice(n*20.0/self.FS, n*10000.0/self.FS)
w = fftfreq(n,1.0/self.FS)[cut]
pl.semilogx(w, spec(y))
if isinstance(lbl, basestring):
labels.append(lbl)
else:
labels.extend(lbl)
self.finish_plot(labels, loc='upper left')
def make_sweep(self,
pre=None,
span=0.5,
post=0.1,
magnitude=1e-2,
start=20,
stop=10000):
smpl = lambda tm: int(round(tm * self.FS))
if pre is None:
pre = span / 2
s, d = dk_lib.genlogsweep(start, stop, self.FS, smpl(pre), smpl(span),
smpl(post))
s *= magnitude
n = dk_lib.pow2roundup(len(s))
#d /= np.mean(abs(np.fft.fft(dk_lib.fft_convolve(d, s), n))[n*start/self.FS:n*stop/self.FS])
#return s, d
return s
def _default_make_spectrum(self, response, freqlist):
n = dk_lib.pow2roundup(len(response))
return np.fft.rfft(response, n, axis=0)
def _default_make_spectrum(self, response, freqlist):
n = dk_lib.pow2roundup(len(response))
return numpy.fft.rfft(response, n, axis=0)
