plt.plot(time,signal,'k') plt.plot(time,sum(book['reconstruction']).real , 'r') plt.subplot(4,1,2) plt.plot(time,book['reconstruction'][0].real , 'r') plt.subplot(4,1,3) plt.plot(time,book['reconstruction'][1].real , 'r') plt.subplot(4,1,4) plt.plot(time,book['reconstruction'][2].real , 'r') plt.show() # draw standard t-f map (T,F,TFmap) = calculateTFMap(book,time,config['samplingFrequency'],0) gs = gridspec.GridSpec(3,1,height_ratios=[3,1,1]) fig = plt.figure() ax0 = plt.subplot(gs[0]) m = ax0.imshow(np.abs(TFmap) , aspect='auto' , origin='lower' , extent=[0.0,numberOfSamples/samplingFrequency , 0.0,samplingFrequency/2]) ax0.set_xlabel(r'Time [s]') ax0.set_ylabel(r'Frequency [Hz]') # ax0.set_ylim(config['mapFreqRange']) ax1 = plt.subplot(gs[1]) ax1.plot(time,signal) ax1.set_xlabel(r'Time [s]') ax1.set_ylabel(r'Amplitude [uV]')
def replot(self):
time = np.arange(0,self.books[self.nameOfBook]['originalData'].shape[2]) / self.books[self.nameOfBook]['config']['samplingFrequency']
tmp_time = np.arange(0,self.books[self.nameOfBook]['originalData'].shape[2])
x_fromWhere = 0
x_toWhere = self.books[self.nameOfBook]['originalData'].shape[2] / self.books[self.nameOfBook]['config']['samplingFrequency']
if self.flags['channel'] == 1 or self.flags['trial'] == 1:
self.decompositionPlot.ax1.plot(time , np.squeeze(self.books[self.nameOfBook]['originalData'][self.trialsCalculated[self.trial]-1,self.channelsCalculated[self.channel]-1,:]) , 'k')
self.decompositionPlot.ax1.set_title('Original signal')
self.decompositionPlot.ax1.hold(False)
self.decompositionPlot.ax1.set_ylabel(r'Amplitude [au]')
self.decompositionPlot.ax1.set_xlim([x_fromWhere , x_toWhere])
(y_fromWhere,y_toWhere) = self.decompositionPlot.ax1.get_ylim()
limits = self.integrateLimits()
reconstruction = getReconstruction(self.books[self.nameOfBook]['book'][self.trial,self.channel] , tmp_time , limits)
self.decompositionPlot.ax2.plot(time , reconstruction , 'k')
self.decompositionPlot.ax2.set_xlim([x_fromWhere , x_toWhere])
self.decompositionPlot.ax2.set_ylim([y_fromWhere , y_toWhere])
self.decompositionPlot.ax2.set_title('Decomposition')
self.decompositionPlot.ax2.hold(False)
self.decompositionPlot.ax2.set_ylabel(r'Amplitude [au]')
if self.flags['atom'] == 1:
func = getAtomReconstruction(self.books[self.nameOfBook]['book'][self.trial,self.channel].iloc[self.atom] , tmp_time)
self.decompositionPlot.ax3.plot(time , func , 'k')
self.decompositionPlot.ax3.set_xlim([x_fromWhere , x_toWhere])
(y_fromWhere,y_toWhere) = self.decompositionPlot.ax1.get_ylim()
self.decompositionPlot.ax3.set_ylim([y_fromWhere , y_toWhere])
self.decompositionPlot.ax3.set_title('Single function')
self.decompositionPlot.ax3.hold(False)
self.decompositionPlot.ax3.set_ylabel(r'Amplitude [au]')
self.decompositionPlot.ax3.set_xlabel(r'Time [s]')
self.decompositionPlot.draw()
if self.flags['channel'] == 1 or self.flags['trial'] == 1:
(self.T,self.F,self.TFmap) = calculateTFMap(self.books[self.nameOfBook]['book'][self.trial,self.channel] , tmp_time , self.books[self.nameOfBook]['config']['samplingFrequency'] , 0)
self.amplitudeMapPlot.ax0.imshow(np.abs(self.TFmap) , aspect='auto' , origin='lower' , extent=[x_fromWhere,x_toWhere , 0.0,self.books[self.nameOfBook]['config']['samplingFrequency']/2.])
self.amplitudeMapPlot.ax0.hold(False)
self.amplitudeMapPlot.ax0.set_ylabel(r'Frequency [Hz]')
self.amplitudeMapPlot.ax1.clear()
self.amplitudeMapPlot.ax1.plot(time , np.squeeze(self.books[self.nameOfBook]['originalData'][self.trialsCalculated[self.trial]-1,self.channelsCalculated[self.channel]-1,:]) , 'k')
self.amplitudeMapPlot.ax1.plot(time , reconstruction , 'r')
self.amplitudeMapPlot.ax1.set_xlim([x_fromWhere , x_toWhere])
self.amplitudeMapPlot.ax1.set_ylabel(r'Amplitude [au]')
(y_fromWhere,y_toWhere) = self.amplitudeMapPlot.ax1.get_ylim()
if self.flags['atom'] == 1:
self.amplitudeMapPlot.ax2.plot(time , func , 'r')
self.amplitudeMapPlot.ax2.set_xlim([x_fromWhere , x_toWhere])
self.amplitudeMapPlot.ax2.set_ylim([y_fromWhere , y_toWhere])
self.amplitudeMapPlot.ax2.hold(False)
self.amplitudeMapPlot.ax2.set_xlabel(r'Time [s]')
self.amplitudeMapPlot.ax2.set_ylabel(r'Amplitude [au]')
self.amplitudeMapPlot.draw()
self.flags['atom'] = 0
self.flags['channel'] = 0
self.flags['trial'] = 0
plt.plot(time, signal, 'k')
plt.plot(time, sum(book['reconstruction']).real, 'r')
plt.subplot(4, 1, 2)
plt.plot(time, book['reconstruction'][0].real, 'r')
plt.subplot(4, 1, 3)
plt.plot(time, book['reconstruction'][1].real, 'r')
plt.subplot(4, 1, 4)
plt.plot(time, book['reconstruction'][2].real, 'r')
plt.show()
# draw standard t-f map
(T, F, TFmap) = calculateTFMap(book, time, config['samplingFrequency'], 0)
gs = gridspec.GridSpec(3, 1, height_ratios=[3, 1, 1])
fig = plt.figure()
ax0 = plt.subplot(gs[0])
m = ax0.imshow(np.abs(TFmap),
aspect='auto',
origin='lower',
extent=[
0.0, numberOfSamples / samplingFrequency, 0.0,
samplingFrequency / 2
])
ax0.set_xlabel(r'Time [s]')
ax0.set_ylabel(r'Frequency [Hz]')
def replot(self):
time = np.arange(
0, self.books[self.nameOfBook]['originalData'].shape[2]
) / self.books[self.nameOfBook]['config']['samplingFrequency']
tmp_time = np.arange(
0, self.books[self.nameOfBook]['originalData'].shape[2])
x_fromWhere = 0
x_toWhere = self.books[self.nameOfBook]['originalData'].shape[
2] / self.books[self.nameOfBook]['config']['samplingFrequency']
if self.flags['channel'] == 1 or self.flags['trial'] == 1:
self.decompositionPlot.ax1.plot(
time,
np.squeeze(self.books[self.nameOfBook]['originalData'][
self.trialsCalculated[self.trial] - 1,
self.channelsCalculated[self.channel] - 1, :]), 'k')
self.decompositionPlot.ax1.set_title('Original signal')
self.decompositionPlot.ax1.hold(False)
self.decompositionPlot.ax1.set_ylabel(r'Amplitude [au]')
self.decompositionPlot.ax1.set_xlim([x_fromWhere, x_toWhere])
(y_fromWhere, y_toWhere) = self.decompositionPlot.ax1.get_ylim()
limits = self.integrateLimits()
reconstruction = getReconstruction(
self.books[self.nameOfBook]['book'][self.trial, self.channel],
tmp_time, limits)
self.decompositionPlot.ax2.plot(time, reconstruction, 'k')
self.decompositionPlot.ax2.set_xlim([x_fromWhere, x_toWhere])
self.decompositionPlot.ax2.set_ylim([y_fromWhere, y_toWhere])
self.decompositionPlot.ax2.set_title('Decomposition')
self.decompositionPlot.ax2.hold(False)
self.decompositionPlot.ax2.set_ylabel(r'Amplitude [au]')
if self.flags['atom'] == 1:
func = getAtomReconstruction(
self.books[self.nameOfBook]['book'][
self.trial, self.channel].iloc[self.atom], tmp_time)
self.decompositionPlot.ax3.plot(time, func, 'k')
self.decompositionPlot.ax3.set_xlim([x_fromWhere, x_toWhere])
(y_fromWhere, y_toWhere) = self.decompositionPlot.ax1.get_ylim()
self.decompositionPlot.ax3.set_ylim([y_fromWhere, y_toWhere])
self.decompositionPlot.ax3.set_title('Single function')
self.decompositionPlot.ax3.hold(False)
self.decompositionPlot.ax3.set_ylabel(r'Amplitude [au]')
self.decompositionPlot.ax3.set_xlabel(r'Time [s]')
self.decompositionPlot.draw()
if self.flags['channel'] == 1 or self.flags['trial'] == 1:
(self.T, self.F, self.TFmap) = calculateTFMap(
self.books[self.nameOfBook]['book'][self.trial,
self.channel], tmp_time,
self.books[self.nameOfBook]['config']['samplingFrequency'], 0)
self.amplitudeMapPlot.ax0.imshow(
np.abs(self.TFmap),
aspect='auto',
origin='lower',
extent=[
x_fromWhere, x_toWhere, 0.0,
self.books[self.nameOfBook]['config']['samplingFrequency']
/ 2.
])
self.amplitudeMapPlot.ax0.hold(False)
self.amplitudeMapPlot.ax0.set_ylabel(r'Frequency [Hz]')
self.amplitudeMapPlot.ax1.clear()
self.amplitudeMapPlot.ax1.plot(
time,
np.squeeze(self.books[self.nameOfBook]['originalData'][
self.trialsCalculated[self.trial] - 1,
self.channelsCalculated[self.channel] - 1, :]), 'k')
self.amplitudeMapPlot.ax1.plot(time, reconstruction, 'r')
self.amplitudeMapPlot.ax1.set_xlim([x_fromWhere, x_toWhere])
self.amplitudeMapPlot.ax1.set_ylabel(r'Amplitude [au]')
(y_fromWhere, y_toWhere) = self.amplitudeMapPlot.ax1.get_ylim()
if self.flags['atom'] == 1:
self.amplitudeMapPlot.ax2.plot(time, func, 'r')
self.amplitudeMapPlot.ax2.set_xlim([x_fromWhere, x_toWhere])
self.amplitudeMapPlot.ax2.set_ylim([y_fromWhere, y_toWhere])
self.amplitudeMapPlot.ax2.hold(False)
self.amplitudeMapPlot.ax2.set_xlabel(r'Time [s]')
self.amplitudeMapPlot.ax2.set_ylabel(r'Amplitude [au]')
self.amplitudeMapPlot.draw()
self.flags['atom'] = 0
self.flags['channel'] = 0
self.flags['trial'] = 0
# config['flags']['drawSingleMaps'] = 0
# config['flags']['saveSingleMaps'] = 0
config['mapFreqRange'] = [0.0, 64.0]
config['mapStructFreqs'] = [0.0, 64.0]
config['mapStructSigmas'] = [0.0, 20.0]
dictionary = generateDictionary(time, config)
for ind1 in np.arange(data.shape[0]):
book = calculateMP(dictionary, data[ind1, :], config)
# print book
break
(T, F, TFmap) = calculateTFMap(book, time, config['samplingFrequency'], 0,
config['mapStructFreqs'],
config['mapStructSigmas'])
# results = {}
# results['mapM'] = TFmap
# results['mapT'] = time
# results['mapF'] = F
fig = plt.figure()
plt.subplot(3, 1, 1)
m = plt.imshow(np.abs(TFmap),
aspect='auto',
origin='lower',
extent=[
0.0, numberOfSamples / samplingFrequency, 0.0,
samplingFrequency / 2
# config['flags']['drawSingleMaps'] = 0
# config['flags']['saveSingleMaps'] = 0
config['mapFreqRange'] = [0.0 , 64.0]
config['mapStructFreqs'] = [0.0 , 64.0]
config['mapStructSigmas'] = [0.0 , 20.0]
dictionary = generateDictionary(time , config)
for ind1 in np.arange(data.shape[0]):
book = calculateMP(dictionary , data[ind1,:] , config)
# print book
break
(T,F,TFmap) = calculateTFMap(book,time,config['samplingFrequency'],0,config['mapStructFreqs'],config['mapStructSigmas'])
# results = {}
# results['mapM'] = TFmap
# results['mapT'] = time
# results['mapF'] = F
fig = plt.figure()
plt.subplot(3,1,1)
m = plt.imshow(np.abs(TFmap) , aspect='auto' , origin='lower' , extent=[0.0,numberOfSamples/samplingFrequency , 0.0,samplingFrequency/2])
plt.ylim(config['mapFreqRange'])
plt.subplot(3,1,2)
plt.plot(data[0,:],'r')
# m_r = plt.imshow(TFmap.real , aspect='auto' , origin='lower' , extent=[0.0,numberOfSamples/samplingFrequency , 0.0,samplingFrequency/2])
# plt.ylim(config['mapFreqRange'])
