ax2.set_ylabel('INL [LSB]')
ax2.annotate(' max INL = %0.2f \n min INL = %0.2f ' %(max_inl, min_inl), \
xy=(0.65,0.7),xycoords='axes fraction', textcoords='offset points', size=22, bbox=dict(boxstyle="round", fc=(1.0, 1.0, 1.0), ec="none"))
plt.xlabel('ADC Code')
plt.tight_layout()
figure_name = lin_dir + "DNL_INL_%s_%s_ch%d"%(env,refs,chnno) + ".png"
print (figure_name)
plt.savefig(figure_name)
plt.close()
#Write misscodes in text log
if(misscodes > 0):
cq.pass_log('WARNING: Number of misscodes found = %d' %misscodes)
#Save DNLs and INLs to characterization table
if(refs == "BJT"):
file_table = rawdir + "Channel_Characterization_BJT_ADC0.csv"
dnl_adc0 = dnl_all[0:8]
dnl_adc0.insert(0,"Worst DNL (%s Ms/s)"%adc_sample_rate)
inl_adc0 = inl_all[0:8]
inl_adc0.insert(0,"Worst INL (%s Ms/s)"%adc_sample_rate)
with open(file_table, 'a', newline='') as csvfile:
csvwriter = csv.writer(csvfile)
csvwriter.writerow(dnl_adc0)
csvwriter.writerow(inl_adc0)
csvfile.close()
file_table = rawdir + "Channel_Characterization_BJT_ADC1.csv"
if (chnno == 12 or chnno == 13 or chnno == 14 or chnno == 15):
ax.set_xlabel("ADC Output / LSB", size=14)
ax.legend(loc="lower center", fontsize='large')
fig.suptitle("RMS Noise: Histogram with " + "%d samples" % N, size=18)
fig.tight_layout()
fig.subplots_adjust(top=0.94)
plt.savefig(noise_dir + "Hist_NoiseTest_%s_%s_%s" % (env, refs, baseline) +
".png")
plt.close()
bad_rms = [x for x in rms if (x > 1.5 or x < 0.1)]
if not (bad_rms == []):
for i in range(len(bad_rms)):
cq.pass_log(
"WARNING: Channel %d RMS Noise (%f) is out of expected range \n" %
(i, bad_rms[i]))
fig = plt.figure(figsize=(16, 9))
plt.title("RMS Noise: All Channels", size=22)
plt.plot(rms, marker='o')
plt.ylim(0.2, 1.2)
plt.yticks(size=18)
plt.xticks(np.arange(0, 16, 1), size=18)
plt.xlabel('Channel', size=18)
plt.ylabel('RMS Noise', size=18)
plt.savefig(noise_dir + "RMS_NoiseTest_%s_%s_%s" % (env, refs, baseline) +
".png")
plt.close()
signal_pwr = signal_pwr + p[mx + k] + p[mx - k]
p_aux[mx + k] = noise
p_aux[mx - k] = noise
##### Extract parameters of interest #####
NAD = np.sum(p) - signal_pwr
SINAD = 10 * np.log10(signal_pwr / NAD)
ENOB = (SINAD - 1.76 + 20 * np.log10(Vfullscale / Vinput)) / 6.02
print(SINAD, ENOB, NAD, 20 * np.log10(Vfullscale / Vinput), Vfullscale,
Vinput)
fundamental = max(p)
SFDR = 10 * np.log10(fundamental / max(p_aux))
if (ENOB < 8):
cq.pass_log("WARNING: Channel %d ENOB = %0.2f bits \n" % (chnno, ENOB))
enob_all.append(ENOB)
##### Plot normalized power spectral density in dBFS #####
fig = plt.figure(figsize=(10, 8))
psd = psd[trunc:Ntot - trunc]
psd_dbfs = psd - max(psd) - 20 * np.log10(Vfullscale / Vinput)
points_dbfs = np.linspace(0, fs / 2, len(psd_dbfs))
plt.plot(points_dbfs, psd_dbfs)
plt.title('%s Environment. %s Reference. Channel %d' % (env, refs, chnno))
plt.xlabel('Frequency [kHz]')
plt.ylabel('Amplitude [dBFS]')
plt.annotate('SFDR = %0.2f dB \nSINAD = %0.2f dB \nENOB = %0.2f bits' %
(np.around(SFDR, decimals=2), np.around(
SINAD, decimals=2), np.around(ENOB, decimals=2)),