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'))