<jupyter_start><jupyter_text>astGl - Uebung 8 - Jitter  Aufgabe 1<jupyter_code>%matplotlib notebook
import math
import numpy as np
import matplotlib.pyplot as plt
from IPython.display import display, Math, Latex
def disp(str):
    display(Latex(str))<jupyter_output><empty_output><jupyter_text>Theorie<jupyter_code>sig_j = 1.0E-12 # psRMS
f = 125E6; # MHz
SJNR = 10*math.log(  1/(8*np.pi**2 * f**2 * sig_j**2) , 10)
float(SJNR) # in dB
ENOB = (SJNR - 1.76) / 6.02 
float(ENOB)<jupyter_output><empty_output><jupyter_text>Aufgabe<jupyter_code>sig_jps =   1.0E-12 # 1psRMS
sig_jfs = 100.0E-15 # 100fsRMS
f = np.logspace(1, 8)
SJNRps = 1/(8*np.pi**2 * f**2 * sig_jps**2)
SJNRps_db = 20*np.log10(SJNRps)
SJNRfs = 1/(8*np.pi**2 * f**2 * sig_jfs**2)
SJNRfs_db = 20*np.log10(SJNRfs)
plt.figure(0)
plt.plot(f, SJNRps_db)
plt.plot(f, SJNRfs_db)
plt.legend(['sigma_j = 1ps', 'sigma_j = 100fs'])
plt.yscale('linear')
plt.xscale('log')
plt.grid(True)
plt.gca().xaxis.grid(True, which='minor')  # minor grid on too
plt.xlabel('f [Hz]')
plt.ylabel('SJNR [dB]')
ENOBps = -20*np.log10(f)/6.02 + 34.96
ENOBfs = -20*np.log10(f)/6.02 + 34.96 + 3.32
plt.figure(1)
plt.plot(f, ENOBps)
plt.plot(f, ENOBfs)
plt.legend(['sigma_j = 1ps', 'sigma_j = 100fs'])
plt.yscale('linear')
plt.xscale('log')
plt.grid(True)
plt.gca().xaxis.grid(True, which='minor')  # minor grid on too
plt.xlabel('f [Hz]')
plt.ylabel('SJNR [dB]')<jupyter_output><empty_output>
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