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SNR figure showing dependence on T also

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Eric Teunis de Boone 2022-11-03 15:39:25 +01:00
parent 68f029f1f7
commit 3e8f3f713b
1 changed files with 20 additions and 1 deletions
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21
fourier/04_signal_to_noise.py
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@ -216,7 +216,7 @@ if __name__ == "__main__":
# plot the snrs # plot the snrs
fig, axs2 = plt.subplots() fig, axs2 = plt.subplots()
fig.basefname="signal_to_noise" fig.basefname="signal_to_noise_vs_N"
axs2.set_xlabel("$N = T*f_s$") axs2.set_xlabel("$N = T*f_s$")
axs2.set_ylabel("SNR") axs2.set_ylabel("SNR")
@ -233,5 +233,24 @@ if __name__ == "__main__":
axs2.legend() axs2.legend()
# plot snrs vs T
fig, axs3 = plt.subplots()
fig.basefname="signal_to_noise_vs_T"
axs3.set_xlabel("time [us]")
axs3.set_ylabel("SNR")
for i, (f_sample, f_sine, t_lengths) in enumerate(fs_iter):
# plot the means
l = axs3.plot(t_lengths, np.mean(my_snrs[i], axis=-1), marker='o', ls='none', label='f:{}MHz, fs:{}MHz'.format(f_sine, f_sample), markeredgecolor='black', markeredgewidth=1)
color = l[0].get_color()
for k, t_length in enumerate(t_lengths):
t_length = np.repeat(t_length , my_snrs.shape[-1])
axs3.plot(t_length, my_snrs[i,k], ls='none', color=color, marker='o', alpha=max(0.01, 1/my_snrs.shape[-1]))
axs3.legend()
### Save or show figures ### Save or show figures
save_all_figs_to_path_or_show(args.fname, default_basename=__file__, default_extensions=default_extensions) save_all_figs_to_path_or_show(args.fname, default_basename=__file__, default_extensions=default_extensions)