<jupyter_start><jupyter_text>Basic behaviourSignificant broadening of spectral peak, preservation of central frequency.<jupyter_code>stretch = 16
freq = 2000
baseMethods.plotPSDpaulstretchSine(freq,stretch,showPlot=False)
plt.xlim([0,5000])
plt.show()
stretch = 16
fshift = 0
freqs = np.array((200,400)) + fshift
#baseMethods.plotPSDpaulstretchHarmonic(freqs,stretch)<jupyter_output><empty_output><jupyter_text>Limits to frequency space resolutionBy testing with peaks at varying proximity, we can show that paulstretch using these settings can distingush frequencies of 200 Hz, but not 100 Hz difference. In practise, this level of distinction is not audible, as the peaks are very broad (and noisy), so there would be no resonances generated which would allow a listener to recognise a difference.<jupyter_code>stretch = 16
lowerDifference = 100
upperDifference = 200
freqs = np.array((200,200+lowerDifference,1000,1000+upperDifference))
baseMethods.plotPSDpaulstretchHarmonic(freqs,stretch,showPlot=False)
plt.xlim([0,3500])
plt.ylim([1e-4,1])
plt.show()
freqs += 10000
baseMethods.plotPSDpaulstretchHarmonic(freqs,stretch,showPlot=False)
plt.xlim([9000,12500])
plt.ylim([1e-4,1])
plt.show()<jupyter_output><empty_output><jupyter_text>Example with many frequenciesNo observed anomalies related to frequencies of large seperation<jupyter_code>stretch = 16
freqs = np.array((200,400,1000,1100,2000,2050,3000,3025,10000,15000,19000,22000))
baseMethods.plotPSDpaulstretchHarmonic(freqs,stretch)
baseMethods.plotPSDpaulstretchHarmonic(freqs,stretch,showPlot=False)
plt.xlim([0,3500])
plt.ylim([1e-4,1])
plt.show()<jupyter_output><empty_output>
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