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https://gitlab.science.ru.nl/mthesis-edeboone/m-thesis-introduction.git
synced 2024-11-13 01:53:31 +01:00
Passband calculates power not amplitude
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2 changed files with 46 additions and 36 deletions
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@ -1,4 +1,5 @@
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#!/usr/bin/env python3
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# vim: fdm=indent ts=4
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__doc__ = \
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"""
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@ -45,31 +46,31 @@ def noisy_sine_realisation_snr(
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# determine signal to noise
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noise_level = bandlevel(noise, f_sample, noise_band)
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noise_power = bandpower(noise, f_sample, noise_band)
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if cut_signal_band_from_noise_band:
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lower_noise_band = passband(noise_band[0], signal_band[0])
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upper_noise_band = passband(signal_band[1], noise_band[1])
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noise_level = bandlevel(noise, f_sample, lower_noise_band)
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noise_level += bandlevel(noise, f_sample, upper_noise_band)
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noise_power = bandpower(noise, f_sample, lower_noise_band)
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noise_power += bandpower(noise, f_sample, upper_noise_band)
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signal_level = bandlevel(samples, f_sample, signal_band)
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signal_power = bandpower(samples, f_sample, signal_band)
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snrs[j] = np.sqrt(signal_level/noise_level)
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snrs[j] = np.sqrt(signal_power/noise_power)
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# make a nice plot showing what ranges were taken
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# and the bandlevels associated with them
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# and the bandpowers associated with them
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if return_ranges_plot and j == 0:
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combined_fft, freqs = ft_spectrum(samples+noise, f_sample)
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freq_scaler=1
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# plot the original signal
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if False:
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_, ax = plt.subplots()
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ax = plot_signal(samples+noise, sample_rate=f_sample/1e6, time_unit='us', ax=ax)
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ax = plot_signal(samples+noise, sample_rate=f_sample/freq_scaler, time_unit='us', ax=ax)
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# plot the spectrum
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if True:
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freq_scaler=1e6
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_, axs = plot_combined_spectrum(combined_fft, freqs, freq_scaler=freq_scaler, freq_unit='MHz')
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# indicate band ranges and frequency
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@ -81,17 +82,22 @@ def noisy_sine_realisation_snr(
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# indicate initial phase
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axs[1].axhline(init_params[2], color='r', alpha=0.4)
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# plot the band levels
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levelax = axs[0].twinx()
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levelax.set_ylabel("Bandlevel")
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levelax.hlines(signal_level, noise_band[0]/freq_scaler, signal_band[1]/freq_scaler, colors=['orange'])
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levelax.hlines(noise_level, noise_band[0]/freq_scaler, noise_band[1]/freq_scaler, colors=['purple'])
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levelax.set_ylim(bottom=0)
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# plot the band powers
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if False:
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powerax = axs[0].twinx()
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powerax.set_ylabel("Bandpower")
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else:
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powerax = axs[0]
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powerax.hlines(np.sqrt(signal_power), noise_band[0]/freq_scaler, noise_band[1]/freq_scaler, colors=['orange'], zorder=5)
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powerax.hlines(np.sqrt(noise_power), noise_band[0]/freq_scaler, noise_band[1]/freq_scaler, colors=['purple'], zorder=5)
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powerax.set_ylim(bottom=0)
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axs[0].legend()
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# plot signal_band pass signal
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if False:
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if True:
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freqs = np.fft.fftfreq(len(samples), 1/f_sample)
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bandmask = bandpass_mask(freqs, band=signal_band)
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fft = np.fft.fft(samples)
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@ -99,7 +105,7 @@ def noisy_sine_realisation_snr(
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bandpassed_samples = np.fft.ifft(fft)
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_, ax3 = plt.subplots()
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ax3 = plot_signal(bandpassed_samples, sample_rate=f_sample/1e6, time_unit='us', ax=ax3)
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ax3 = plot_signal(bandpassed_samples, sample_rate=f_sample/freq_scaler, time_unit='us', ax=ax3)
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ax3.set_title("Bandpassed Signal")
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@ -122,10 +128,10 @@ if __name__ == "__main__":
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args.fname = None
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###
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t_lengths = np.linspace(1e3, 5e4, 5)* 1e-9 # s
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N = 10e1
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fs_sine = [33.3e6, 50e6, 73.3e6] # Hz
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fs_sample = [250e6, 500e6] # Hz
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t_lengths = np.linspace(1, 50, 50) # us
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N = 50e0
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fs_sine = [33.3, 50, 73.3] # MHz
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fs_sample = [250, 500] # MHz
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if False:
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# show t_length and fs_sample really don't care
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fs_iter = [ (fs_sample[0], f_sine, t_lengths) for f_sine in fs_sine ]
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@ -146,7 +152,7 @@ if __name__ == "__main__":
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for i in range(int(N)):
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delta_f = 1/t_length
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signal_band = passband(f_sine- 3*delta_f, f_sine + 3*delta_f)
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noise_band = passband(30e6, 80e6)
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noise_band = passband(30, 80) # MHz
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snrs[i], axs = noisy_sine_realisation_snr(
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N=1,
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@ -161,7 +167,7 @@ if __name__ == "__main__":
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noise_band = noise_band,
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signal_band = signal_band,
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return_ranges_plot=True,
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return_ranges_plot=False,
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rng=rng,
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)
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@ -176,14 +182,17 @@ if __name__ == "__main__":
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plt.show(block=False)
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else:
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#original code
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sine_amp = 1
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noise_sigma = 4
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my_snrs = np.zeros( (len(fs_iter), len(t_lengths), int(N)) )
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for i, (f_sample, f_sine, t_lengths) in enumerate( fs_iter ):
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for k, t_length in enumerate(t_lengths):
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return_ranges_plot = ((k==0) and True) or ( (k==(len(t_lengths)-1)) and True) and i < 1
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return_ranges_plot = ((k==0) and not True) or ( (k==(len(t_lengths)-1)) and True) and i < 1
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delta_f = 1/t_length
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signal_band = passband(f_sine- 3*delta_f, f_sine + 3*delta_f)
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noise_band=passband(30e6, 80e6)
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signal_band = passband( *(f_sine + 2*delta_f*np.array([-1,1])) )
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noise_band=passband(30, 80) # MHz
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my_snrs[i,k], axs = noisy_sine_realisation_snr(
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N=N,
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@ -192,8 +201,8 @@ if __name__ == "__main__":
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# signal properties
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f_sine = f_sine,
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sine_amp = 1,
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noise_sigma = 1,
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sine_amp = sine_amp,
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noise_sigma = noise_sigma,
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noise_band = noise_band,
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signal_band = signal_band,
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@ -207,12 +216,13 @@ if __name__ == "__main__":
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# plot the snrs
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fig, axs2 = plt.subplots()
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fig.basefname="signal_to_noise"
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axs2.set_xlabel("$N = T*f_s$")
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axs2.set_ylabel("SNR")
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for i, (f_sample, f_sine, t_lengths) in enumerate(fs_iter):
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# plot the means
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l = axs2.plot(t_lengths*f_sample, np.mean(my_snrs[i], axis=-1), marker='*', ls='none', label='f:{}MHz, fs:{}MHz'.format(f_sine/1e6, f_sample/1e6), markeredgecolor='black')
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l = axs2.plot(t_lengths*f_sample, np.mean(my_snrs[i], axis=-1), marker='*', ls='none', label='f:{}MHz, fs:{}MHz'.format(f_sine, f_sample), markeredgecolor='black')
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color = l[0].get_color()
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@ -16,9 +16,9 @@ class passband(namedtuple("passband", ['low', 'high'], defaults=[0, np.inf])):
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def freq_mask(frequencies):
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return bandpass_mask(frequencies, self)
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def signal_level(samples, samplerate, normalise_bandsize=True, **ft_kwargs):
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def signal_power(samples, samplerate, normalise_bandsize=True, **ft_kwargs):
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return bandlevel(samples, samplerate, self, normalise_bandsize, **ft_kwargs)
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return bandpower(samples, samplerate, self, normalise_bandsize, **ft_kwargs)
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def filter_samples(samples, samplerate, **ft_kwargs):
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"""
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@ -52,7 +52,7 @@ def bandpass_mask(freqs, band=passband()):
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def bandsize(band = passband()):
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return band[1] - band[0]
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def bandlevel(samples, samplerate=1, band=passband(), normalise_bandsize=True, **ft_kwargs):
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def bandpower(samples, samplerate=1, band=passband(), normalise_bandsize=True, **ft_kwargs):
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fft, freqs = ft_spectrum(samples, samplerate, **ft_kwargs)
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bandmask = bandpass_mask(freqs, band=band)
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@ -62,9 +62,9 @@ def bandlevel(samples, samplerate=1, band=passband(), normalise_bandsize=True, *
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else:
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bins = 1
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level = np.sum(np.abs(fft[bandmask])**2)
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power = np.sum(np.abs(fft[bandmask])**2)
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return level/bins
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return power/bins
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def signal_to_noise( samplerate, samples, noise, signal_band, noise_band=None):
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if noise_band is None:
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@ -73,9 +73,9 @@ def signal_to_noise( samplerate, samples, noise, signal_band, noise_band=None):
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if noise is None:
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noise = samples
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noise_level = bandlevel(noise, samplerate, noise_band)
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noise_power = bandpower(noise, samplerate, noise_band)
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signal_level = bandlevel(samples, samplerate, signal_band)
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signal_power = bandpower(samples, samplerate, signal_band)
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return (signal_level/noise_level)**0.5
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return (signal_power/noise_power)**0.5
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