def PlotFFTInput(self):
"""Plot Time Domain of FFT Input Slice for Last Measurement"""
fig = self.CreateFigure("FFT Input")
sp = fig.add_subplot(111)
xaxis = range(0,self.fftn)
sp.plot(xaxis,[x.real for x in self.fftia],'.-',color='b',label='I')
sp.plot(xaxis,[x.imag for x in self.fftia],'.-',color='r',label='Q')
sp.set_ylabel("Magnitude")
sp.set_xlabel("Sample")
#sp.legend(bbox_to_anchor=(1,-0.1))
sp.legend(loc=2,bbox_to_anchor=(0,-0.1),ncol=4)
plt.show()
def PlotFD(self,dbfs=True):
"""Plot Frequency Domain for Last Measurement"""
freqspectrum = np.abs(self.fftoa)
freqspectrum = np.concatenate( [freqspectrum[self.fftn/2:self.fftn],freqspectrum[0:self.fftn/2]] )
if dbfs:
zerodb = 20*np.log10(self.fftn/2)
freqspectrum = (20*np.log10(abs(freqspectrum))) - zerodb
fig = self.CreateFigure("Frequency Domain")
sp = fig.add_subplot(111)
xaxis = np.r_[0:self.fftn] * (self.Sr/self.fftn)
xaxis = np.concatenate( [(xaxis[self.fftn/2:self.fftn] - self.Sr),xaxis[0:self.fftn/2]])
sp.plot(xaxis,freqspectrum,'.-',color='b',label='Spectrum')
sp.set_ylabel("dBFS")
sp.set_xlabel("Frequency")
sp.legend(loc=2,bbox_to_anchor=(0,-0.1),ncol=4)
plt.show()
def PlotTD(self):
"""Plot Time Domain of Jack Input and Output Arrays for Last Measurement"""
fig = self.CreateFigure("Time Domain")
sp = fig.add_subplot(111)
xaxis = range(0,len(self.iIa))
sp.plot(xaxis,self.iIa,'.-',color='b',label='iI')
## 180 phase shift as complex portion is created with -1j
sp.plot(xaxis,-1*self.iQa,'.-',color='r',label='iQ')
sp.plot(xaxis,self.oIa,'.-',color='c',label='oI')
sp.plot(xaxis,self.oQa,'.-',color='m',label='oQ')
## Identify RTFrames
maxy = self.oIa.max()
sp.plot([self.rtframes,self.rtframes],[-maxy,maxy],'k-',lw=3,label='RT Frames')
## Identify Sync Index
sp.plot([self.synci+self.sync2fft,self.synci+self.sync2fft],[-maxy,maxy],'g-',lw=3,label='FFT Start')
sp.plot([self.synci+self.sync2fft+self.fftn,self.synci+self.sync2fft+self.fftn],[-maxy,maxy],'y-',lw=3,label='FFT End')
sp.set_ylabel("Magnitude")
sp.set_xlabel("Sample")
#sp.legend(bbox_to_anchor=(1,-0.1))
sp.legend(loc=2,bbox_to_anchor=(0,-0.1),ncol=7)
plt.show()
# fit and plot a new linear model
regr = linear_model.LinearRegression()
regr.fit(pd.DataFrame(x),pd.DataFrame(y))
x_prime = np.linspace(x.min(),x.max(),num=10)[:,np.newaxis]
y_hat = regr.predict(x_prime)
sp.plot(x_prime,y_hat,linewidth=2,linestyle='dashed',color='green',alpha=0.8)
ss_res = regr.residues_[0]
ss_tot = np.sum((y - np.mean(y))**2)
rsq = 1 - (ss_res/ss_tot)
sp.annotate('R^2: {:.2f}\nCoef: {:.2f}\nIntr: {:.2f}'.format(rsq,regr.coef_[0][0],regr.intercept_[0])
,xy=(1,0),xycoords='axes fraction',xytext=(-12,6),textcoords='offset points'
,color='green',weight='bold',size=12,ha='right',va='bottom')
sp.set_ylabel('Post-test Score')
sp.set_xlabel('Pre-test Score')
sp.axes.grid(True,linestyle='-',color='lightgrey')
plt.subplots_adjust(top=0.95)
plt.show()
## TODO: ## scores pre vs post for test type
# <codecell>
## What's the range of deltas?
df['staard_score_delta'] = df['staard_score_post'] - df['staard_score_pre']
df.boxplot(column='staard_score_delta',by='testtype',sym='k+',vert=False
,widths=0.8,notch=True,bootstrap=1000,figsize=[12,3.5])
