mirror of
https://gitlab.science.ru.nl/mthesis-edeboone/m-thesis-introduction.git
synced 2024-12-21 19:13:32 +01:00
SNR figure: return power from bandlevel: sum(fft**2)
This commit is contained in:
parent
820f58d901
commit
d23f8adff2
1 changed files with 47 additions and 45 deletions
|
|
@ -2,7 +2,7 @@
|
|||
|
||||
__doc__ = \
|
||||
"""
|
||||
Show
|
||||
Show the curve for signal-to-noise ratio vs N_samples
|
||||
"""
|
||||
|
||||
from collections import namedtuple
|
||||
|
|
@ -18,7 +18,7 @@ passband = namedtuple("Band", ['low', 'high'], defaults=[0, np.inf])
|
|||
|
||||
def get_freq_spec(val,dt):
|
||||
"""From earsim/tools.py"""
|
||||
fval = np.abs(np.fft.fft(val))[:len(val)//2]
|
||||
fval = np.fft.fft(val)[:len(val)//2]
|
||||
freq = np.fft.fftfreq(len(val),dt)[:len(val)//2]
|
||||
return fval, freq
|
||||
|
||||
|
|
@ -30,11 +30,11 @@ def ft_spectrum( signal, sample_rate=1, ftfunc=None, freqfunc=None, mask_bias=Fa
|
|||
return get_freq_spec(signal, 1/sample_rate)
|
||||
|
||||
n_samples = len(signal)
|
||||
|
||||
|
||||
if ftfunc is None:
|
||||
real_signal = np.isrealobj(signal)
|
||||
if False and real_signal:
|
||||
ftfunc = ft.rfft
|
||||
ftfunc = ft.rfft
|
||||
freqfunc = ft.rfftfreq
|
||||
else:
|
||||
ftfunc = ft.fft
|
||||
|
|
@ -44,16 +44,16 @@ def ft_spectrum( signal, sample_rate=1, ftfunc=None, freqfunc=None, mask_bias=Fa
|
|||
freqfunc = ft.fftfreq
|
||||
|
||||
normalisation = 2/len(signal) if normalise_amplitude else 1
|
||||
|
||||
|
||||
spectrum = normalisation * ftfunc(signal)
|
||||
freqs = freqfunc(n_samples, 1/sample_rate)
|
||||
|
||||
|
||||
if not mask_bias:
|
||||
return spectrum, freqs
|
||||
else:
|
||||
return spectrum[1:], freqs[1:]
|
||||
|
||||
|
||||
|
||||
def plot_spectrum( spectrum, freqs, plot_complex=False, plot_power=False, plot_amplitude=None, ax=None, freq_unit="Hz", freq_scaler=1):
|
||||
""" Plot a signal's spectrum on an Axis object"""
|
||||
plot_amplitude = plot_amplitude or (not plot_power and not plot_complex)
|
||||
|
|
@ -61,7 +61,7 @@ def plot_spectrum( spectrum, freqs, plot_complex=False, plot_power=False, plot_a
|
|||
|
||||
if ax is None:
|
||||
ax = plt.gca()
|
||||
|
||||
|
||||
ax.set_title("Spectrum")
|
||||
ax.set_xlabel("f" + (" ["+freq_unit+"]" if freq_unit else "" ))
|
||||
ylabel = ""
|
||||
|
|
@ -80,7 +80,7 @@ def plot_spectrum( spectrum, freqs, plot_complex=False, plot_power=False, plot_a
|
|||
|
||||
if plot_power:
|
||||
ax.plot(freqs/freq_scaler, np.abs(spectrum)**2, '.-', label='Power', alpha=alpha)
|
||||
|
||||
|
||||
if plot_amplitude:
|
||||
ax.plot(freqs/freq_scaler, np.abs(spectrum), '.-', label='Abs', alpha=alpha)
|
||||
|
||||
|
|
@ -97,7 +97,7 @@ def plot_phase( spectrum, freqs, ylim_epsilon=0.5, ax=None, freq_unit="Hz", freq
|
|||
|
||||
ax.plot(freqs/freq_scaler, np.angle(spectrum), '.-')
|
||||
ax.set_ylim(-1*np.pi - ylim_epsilon, np.pi + ylim_epsilon)
|
||||
|
||||
|
||||
return ax
|
||||
|
||||
def plot_signal( signal, sample_rate = 1, ax=None, time=None, time_unit="s", **kwargs):
|
||||
|
|
@ -112,13 +112,13 @@ def plot_signal( signal, sample_rate = 1, ax=None, time=None, time_unit="s", **k
|
|||
ax.set_ylabel("A(t)")
|
||||
|
||||
ax.plot(time, signal, **kwargs)
|
||||
|
||||
|
||||
return ax
|
||||
|
||||
def plot_combined_spectrum(spectrum, freqs,
|
||||
def plot_combined_spectrum(spectrum, freqs,
|
||||
spectrum_kwargs={}, fig=None, gs=None, freq_scaler=1, freq_unit="Hz"):
|
||||
"""Plot both the frequencies and phase in one figure."""
|
||||
|
||||
|
||||
# configure plotting layout
|
||||
if fig is None:
|
||||
fig = plt.figure(figsize=(8, 16))
|
||||
|
|
@ -130,8 +130,8 @@ def plot_combined_spectrum(spectrum, freqs,
|
|||
ax2 = fig.add_subplot(gs[-1, -1], sharex=ax1)
|
||||
|
||||
axes = np.array([ax1, ax2])
|
||||
|
||||
# plot the spectrum
|
||||
|
||||
# plot the spectrum
|
||||
plot_spectrum(spectrum, freqs, ax=ax1, freq_scaler=freq_scaler, freq_unit=freq_unit, **spectrum_kwargs)
|
||||
|
||||
# plot the phase
|
||||
|
|
@ -139,13 +139,13 @@ def plot_combined_spectrum(spectrum, freqs,
|
|||
|
||||
ax1.xaxis.tick_top()
|
||||
[label.set_visible(False) for label in ax1.get_xticklabels()]
|
||||
|
||||
|
||||
return fig, axes
|
||||
|
||||
|
||||
def phasemod(phase, low=np.pi):
|
||||
"""
|
||||
Modulo phase such that it falls within the
|
||||
Modulo phase such that it falls within the
|
||||
interval $[-low, 2\pi - low)$.
|
||||
"""
|
||||
return (phase + low) % (2*np.pi) - low
|
||||
|
|
@ -153,7 +153,7 @@ def phasemod(phase, low=np.pi):
|
|||
def save_all_figs_to_path(fnames, figs=None, default_basename=__file__, default_extensions=['.pdf', '.png']):
|
||||
if figs is None:
|
||||
figs = [plt.figure(i) for i in plt.get_fignums()]
|
||||
|
||||
|
||||
default_basename = path.basename(default_basename)
|
||||
|
||||
# singular value
|
||||
|
|
@ -163,7 +163,8 @@ def save_all_figs_to_path(fnames, figs=None, default_basename=__file__, default_
|
|||
if len(fnames) == len(figs):
|
||||
fnames_list = zip(figs, fnames, False)
|
||||
elif len(fnames) == 1:
|
||||
fnames_list = ( (fig, fnames[0], len(figs) > 1) for fig in figs)
|
||||
tmp_fname = fnames[0] #needed for generator
|
||||
fnames_list = ( (fig, tmp_fname, len(figs) > 1) for fig in figs)
|
||||
else:
|
||||
# outer product magic
|
||||
fnames_list = ( (fig,fname, False) for fname in fnames for fig in figs )
|
||||
|
|
@ -224,7 +225,7 @@ def bandlevel(samples, samplerate=1, band=passband(), normalise_bandsize=True, *
|
|||
else:
|
||||
bins = 1
|
||||
|
||||
level = np.sum(np.abs(fft[bandmask]))
|
||||
level = np.sum(np.abs(fft[bandmask])**2)
|
||||
|
||||
return level/bins
|
||||
|
||||
|
|
@ -265,7 +266,7 @@ def main(
|
|||
for j in range(N):
|
||||
samples, noise = noisy_sine_sampling(time, init_params, noise_sigma)
|
||||
|
||||
|
||||
|
||||
# determine signal to noise
|
||||
noise_level = bandlevel(noise, f_sample, noise_band)
|
||||
if cut_signal_band_from_noise_band:
|
||||
|
|
@ -277,7 +278,7 @@ def main(
|
|||
|
||||
signal_level = bandlevel(samples, f_sample, signal_band)
|
||||
|
||||
snrs[j] = signal_level/noise_level
|
||||
snrs[j] = np.sqrt(signal_level/noise_level)
|
||||
|
||||
# make a nice plot showing what ranges were taken
|
||||
# and the bandlevels associated with them
|
||||
|
|
@ -293,23 +294,23 @@ def main(
|
|||
if True:
|
||||
freq_scaler=1e6
|
||||
_, axs = plot_combined_spectrum(combined_fft, freqs, freq_scaler=freq_scaler, freq_unit='MHz')
|
||||
|
||||
|
||||
# indicate band ranges and frequency
|
||||
for ax in axs:
|
||||
ax.axvline(f_sine/freq_scaler, color='r', alpha=0.4)
|
||||
ax.axvspan(noise_band[0]/freq_scaler, noise_band[1]/freq_scaler, color='purple', alpha=0.3, label='noiseband')
|
||||
ax.axvspan(signal_band[0]/freq_scaler, signal_band[1]/freq_scaler, color='orange', alpha=0.3, label='signalband')
|
||||
|
||||
|
||||
# indicate initial phase
|
||||
axs[1].axhline(init_params[2], color='r', alpha=0.4)
|
||||
|
||||
|
||||
# plot the band levels
|
||||
levelax = axs[0].twinx()
|
||||
levelax.set_ylabel("Bandlevel")
|
||||
levelax.hlines(signal_level, noise_band[0]/freq_scaler, signal_band[1]/freq_scaler, colors=['orange'])
|
||||
levelax.hlines(noise_level, noise_band[0]/freq_scaler, noise_band[1]/freq_scaler, colors=['purple'])
|
||||
levelax.set_ylim(bottom=0)
|
||||
|
||||
|
||||
axs[0].legend()
|
||||
|
||||
# plot signal_band pass signal
|
||||
|
|
@ -319,7 +320,7 @@ def main(
|
|||
fft = np.fft.fft(samples)
|
||||
fft[ ~bandmask ] = 0
|
||||
bandpassed_samples = np.fft.ifft(fft)
|
||||
|
||||
|
||||
_, ax3 = plt.subplots()
|
||||
ax3 = plot_signal(bandpassed_samples, sample_rate=f_sample/1e6, time_unit='us', ax=ax3)
|
||||
ax3.set_title("Bandpassed Signal")
|
||||
|
|
@ -346,12 +347,12 @@ if __name__ == "__main__":
|
|||
###
|
||||
t_lengths = np.linspace(1e3, 5e4)* 1e-9 # s
|
||||
N = 10e1
|
||||
f_sine = 53e6 # Hz
|
||||
f_sine = 53.3e6 # Hz
|
||||
f_sample = 250e6 # Hz
|
||||
|
||||
if True:
|
||||
N = 2 # Note: keep this low, N figures will be displayed!
|
||||
N_t_length = 2
|
||||
if False:
|
||||
N = 1 # Note: keep this low, N figures will be displayed!
|
||||
N_t_length = 10
|
||||
for t_length in t_lengths[-N_t_length-1:-1]:
|
||||
snrs = np.zeros( int(N))
|
||||
for i in range(int(N)):
|
||||
|
|
@ -360,18 +361,18 @@ if __name__ == "__main__":
|
|||
N=1,
|
||||
t_length=t_length,
|
||||
f_sample=f_sample,
|
||||
|
||||
|
||||
# signal properties
|
||||
f_sine = f_sine,
|
||||
sine_amp = 1,
|
||||
noise_sigma = 1,
|
||||
|
||||
|
||||
noise_band = passband(30e6, 80e6),
|
||||
signal_band = passband(f_sine- 3*delta_f, f_sine + 3*delta_f),
|
||||
|
||||
|
||||
return_ranges_plot=True
|
||||
)
|
||||
|
||||
|
||||
axs[0].set_title("SNR: {}, N:{}".format(snrs[i], t_length*f_sample))
|
||||
axs[0].set_xlim(
|
||||
(f_sine - 20*delta_f)/1e6,
|
||||
|
|
@ -380,43 +381,44 @@ if __name__ == "__main__":
|
|||
|
||||
print(snrs, "M:",np.mean(snrs))
|
||||
|
||||
plt.show(block=True)
|
||||
plt.show(block=False)
|
||||
|
||||
else:
|
||||
#original code
|
||||
my_snrs = np.zeros( (len(t_lengths), int(N)) )
|
||||
for j, t_length in enumerate(t_lengths):
|
||||
return_ranges_plot = ((j==0) and True) or ( (j==(len(t_lengths)-1)) and True)
|
||||
|
||||
|
||||
delta_f = 1/t_length
|
||||
|
||||
|
||||
my_snrs[j], axs = main(
|
||||
N=N,
|
||||
t_length=t_length,
|
||||
f_sample = f_sample,
|
||||
|
||||
|
||||
# signal properties
|
||||
f_sine = f_sine,
|
||||
sine_amp = 1,
|
||||
noise_sigma = 1,
|
||||
|
||||
|
||||
noise_band = passband(30e6, 80e6),
|
||||
signal_band = passband(f_sine- 3*delta_f, f_sine + 3*delta_f),
|
||||
|
||||
|
||||
return_ranges_plot=return_ranges_plot,
|
||||
)
|
||||
|
||||
|
||||
if return_ranges_plot:
|
||||
ranges_axs = axs
|
||||
|
||||
|
||||
fig, axs2 = plt.subplots()
|
||||
axs2.set_xlabel("N = T*$f_s$")
|
||||
axs2.set_ylabel("SNR")
|
||||
|
||||
|
||||
for j, t_length in enumerate(t_lengths):
|
||||
t_length = t_length * f_sample
|
||||
axs2.plot(np.repeat(t_length, my_snrs.shape[1]), my_snrs[j], ls='none', color='blue', marker='o', alpha=max(0.01, 1/my_snrs.shape[1]))
|
||||
axs2.plot(t_length, np.mean(my_snrs[j]), color='green', marker='*', ls='none')
|
||||
# plot the means
|
||||
axs2.plot(t_lengths*f_sample, np.mean(my_snrs, axis=-1), color='green', marker='*', ls='none')
|
||||
|
||||
### Save or show figures
|
||||
if not args.fname:
|
||||
|
|
|
|||
Loading…
Reference in a new issue