def fit_curve(self):
if self.running == True or self.data[0] == []:
return
if (len(self.data[0]) % 2) == 1: # make it an even size, for fitting
self.data[0] = self.data[0][:-1]
self.data[1] = self.data[1][:-1]
fa = em.fit_dsine(self.data[0], self.data[1],
1000.0) # fit in em expects khz
if fa != None:
pa = fa[1]
self.traces.append(
self.pwin.plot(self.data[0],
fa[0],
pen=self.traceCols[self.trial % 5]))
self.trial += 1
ss1 = '%5.2f' % pa[1]
ss2 = '%5.3f' % pa[4]
self.msg(
self.tr('Frequency of Oscillation = ') + ss1 +
self.tr(' Hz. Damping Factor = ') + ss2)
self.history.append((self.data[0], fa[0]))
else:
self.msg(self.tr('Analysis failed. Could not fit data'))
def show_fft(self):
if self.history != []:
x = self.history[-1][0]
y = self.history[-1][1]
if len(x) % 2 != 0:
x = x[:-1]
y = y[:-1]
fa = em.fit_dsine(
np.array(x), np.array(y),
1000) # ./self.guessTP) #Need to fix eyemath, expects kHz
else:
self.msg(self.tr('No data to analyze.'))
return
if fa != None:
if len(self.data[0]) % 2: #odd number
self.data[0] = self.data[0][:-1]
self.data[1] = self.data[1][:-1]
pa = fa[1]
ss = '%5.2f' % pa[1]
self.msg(
self.tr('Sine Fit Result: Frequency ') + ss + self.tr('Hz'))
self.traces.append(
self.pwin.plot(x, fa[0], pen=self.traceCols[self.trial % 5]))
fr = pa[1] # frequency in Hz
dt = np.average(np.diff(self.data[0])) * 1000 #mS
print('freq:%.2f, dt=%.2e' % (fr, dt))
xa, ya = em.fft(self.data[1] - np.average(self.data[1]), dt)
peak = self.peak_index(xa, ya)
ypos = np.max(ya)
pop = pg.plot(xa, ya, pen=self.traceCols[self.trial % 5])
pop.showGrid(x=True, y=True)
txt = pg.TextItem(
text=unicode(self.tr('Fundamental frequency = %5.1f Hz')) %
peak,
color='w')
txt.setPos(peak, ypos)
pop.addItem(txt)
pop.setWindowTitle(self.tr('Frequency Spectrum'))
self.trial += 1
else:
self.msg(self.tr('Failed to fit the curve'))
def fit_curve(self):
if self.history != []:
fa = em.fit_dsine(self.history[-1][0], self.history[-1][1],0)
else:
self.msg(self.tr('No data to analyze.'))
return
if fa != None:
pa = fa[1]
rc = 1.0 / pa[1]
damping = pa[4] / (2*math.pi*pa[1]) # unitless damping factor
ss1 = '%5.2f'%pa[1]
ss2 = '%5.3f'%damping
self.msg(self.tr('Resonant Frequency = ') + ss1 + self.tr(' kHz Damping factor= ') + ss2)
self.traces.append(self.pwin.plot(self.history[-1][0], fa[0], pen = self.traceCols[self.trial%5]))
self.history.append((self.history[-1][0], fa[0]))
self.trial += 1
else:
self.msg(self.tr('Failed to fit the curve'))
def fit_curve(self):
if self.running == True or self.data[0]==[]:
return
if (len(self.data[0])%2) == 1: # make it an even size, for fitting
self.data[0] = self.data[0][:-1]
self.data[1] = self.data[1][:-1]
fa = em.fit_dsine(self.data[0], self.data[1], 1000.0) # fit in em expects khz
if fa != None:
pa = fa[1]
self.traces.append(self.pwin.plot(self.data[0], fa[0], pen = self.traceCols[self.trial%5]))
self.trial += 1
ss1 = '%5.2f'%pa[1]
ss2 = '%5.3f'%pa[4]
self.msg(self.tr('Frequency of Oscillation = ') + ss1 + self.tr(' Hz. Damping Factor = ') + ss2)
self.history.append((self.data[0], fa[0]))
else:
self.msg(self.tr('Analysis failed. Could not fit data'))
def fit_curve(self):
if self.running == True or self.data[0] == []:
return
return
# Make histogram to be added
if (len(self.data[0]) % 2) == 1: # make it an even size, for fitting
self.data[0] = self.data[0][:-1]
self.data[1] = self.data[1][:-1]
fa = em.fit_dsine(self.data[0], self.data[1])
if fa != None:
pa = fa[1]
self.traces.append(self.pwin.plot(self.data[0], fa[0], pen='w'))
self.msg(
'Frequency of Oscillation = %5.2f Hz. Damping Factor = %5.3f' %
(pa[1], pa[4]))
else:
self.msg('Analysis failed. Could not fit data')
def fit_curve(self):
if self.history != []:
x = self.history[-1][0]
y = self.history[-1][1]
if len(x) % 2 != 0:
x = x[:-1]
y = y[:-1]
fa = em.fit_dsine(np.array(x), np.array(y), 1000)# ./self.guessTP) #Need to fix eyemath, expects kHz
else:
self.msg(self.tr('No data to analyze.'))
return
if fa != None:
pa = fa[1]
ss = '%5.2f'%pa[1]
self.msg(self.tr('Sine Fit Result: Frequency ') + ss + self.tr( 'Hz'))
self.traces.append(self.pwin.plot(x, fa[0], pen = self.traceCols[self.trial%5]))
self.trial += 1
else:
self.msg(self.tr('Failed to fit the curve'))
def fit_curve(self):
if self.history != []:
x = self.history[-1][0]
y = self.history[-1][1]
if len(x) % 2 != 0:
x = x[:-1]
y = y[:-1]
fa = em.fit_dsine(np.array(x), np.array(y), 1000)# ./self.guessTP) #Need to fix eyemath, expects kHz
else:
self.msg(self.tr('No data to analyze.'))
return
if fa != None:
pa = fa[1]
ss = '%5.2f'%pa[1]
self.msg(self.tr('Sine Fit Result: Frequency ') + ss + self.tr( 'Hz'))
self.traces.append(self.pwin.plot(x, fa[0], pen = self.traceCols[self.trial%5]))
self.trial += 1
else:
self.msg(self.tr('Failed to fit the curve'))