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https://gitlab.science.ru.nl/mthesis-edeboone/m-thesis-introduction.git
synced 2024-11-13 01:53:31 +01:00
ZH: plot noise parameters if available
+ figlib: histogram with distr fitting
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commit
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2 changed files with 164 additions and 4 deletions
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@ -12,6 +12,8 @@ import numpy as np
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import aa_generate_beacon as beacon
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import lib
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from lib import figlib
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if __name__ == "__main__":
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from os import path
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@ -49,6 +51,7 @@ if __name__ == "__main__":
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####
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fname_dir = args.data_dir
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antennas_fname = path.join(fname_dir, beacon.antennas_fname)
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snr_fname = path.join(fname_dir, beacon.snr_fname)
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fig_dir = args.fig_dir # set None to disable saving
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@ -83,7 +86,8 @@ if __name__ == "__main__":
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N_antennas = len(group.keys())
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# just for funzies
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found_data = np.zeros((N_antennas, 3))
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found_data = np.zeros((N_antennas, 3)) # freq, phase, amp
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noise_data = np.zeros((N_antennas, 2)) # phase, amp
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# Determine frequency and phase
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for i, name in enumerate(group.keys()):
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@ -133,6 +137,7 @@ if __name__ == "__main__":
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# TODO: refine masking
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# use beacon but remove where E_AxB-Beacon != 0
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# Uses the first traces as reference
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t_mask = 0
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if N_mask and orients[0] != 'B':
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N_pre, N_post = N_mask//2, N_mask//2
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@ -166,6 +171,18 @@ if __name__ == "__main__":
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beacon_phases = [ 2*np.pi*t0*f_beacon ]
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amps = [ 3e-7 ]
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# Also try to find the phase from the noise trace if available
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if len(h5ant[traces_key]) > 4:
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noise_trace = h5ant[traces_key][5]
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if np.any(t_mask): # Mask the same area
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noise_trace = noise_trace[t_mask]
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real, imag = lib.direct_fourier_transform(f_beacon, t_trace, noise_trace)
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noise_phase = np.arctan2(imag, real)
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noise_amp = (real**2 + imag**2)**0.5
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noise_data[i] = noise_phase, noise_amp
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# choose highest amp
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idx = np.argmax(amps)
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if False and len(beacon_phases) > 1:
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@ -246,10 +263,16 @@ if __name__ == "__main__":
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h5attrs['amplitude'] = amplitude
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h5attrs['orientation'] = orientation
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if noise_phase:
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h5attrs['noise_phase'] = noise_phase
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h5attrs['noise_amp'] = noise_amp
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print("Beacon Phases, Amplitudes and Frequencies written to", antennas_fname)
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# show histogram of found frequencies
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if show_plots or fig_dir:
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snrs = beacon.read_snr_file(snr_fname)
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if True or allow_frequency_fitting:
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fig, ax = plt.subplots(figsize=figsize)
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ax.set_xlabel("Frequency")
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@ -260,12 +283,45 @@ if __name__ == "__main__":
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fig.savefig(path.join(fig_dir, path.basename(__file__) + f".hist_freq.pdf"))
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if True:
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fig, ax = plt.subplots(figsize=figsize)
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ax.set_xlabel("Amplitudes")
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fig, _ = figlib.fitted_histogram_figure(found_data[:,2], fit_distr=[], mean_snr=snrs['mean'])
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ax = fig.axes[0]
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ax.set_xlabel("Amplitude")
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ax.set_ylabel("Counts")
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ax.hist(found_data[:,2], bins='sqrt', density=False)
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ax.legend()
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if fig_dir:
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fig.savefig(path.join(fig_dir, path.basename(__file__) + f".hist_amp.pdf"))
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if (noise_data[0] != 0).any():
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if True:
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fig, ax = plt.subplots(figsize=figsize)
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ax.set_title("Noise Phases")
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ax.set_xlabel("Phase")
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ax.set_ylabel("#")
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ax.hist(noise_data[:,0], bins='sqrt', density=False)
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ax.legend()
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if fig_dir:
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fig.savefig(path.join(fig_dir, path.basename(__file__) + f".noise.hist_phase.pdf"))
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if True:
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fig, ax = plt.subplots(figsize=figsize)
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ax.set_title("Noise Phase vs Amplitude")
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ax.set_xlabel("Phase")
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ax.set_ylabel("Amplitude [a.u.]")
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ax.plot(noise_data[:,0], noise_data[:,1], ls='none', marker='x')
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if fig_dir:
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fig.savefig(path.join(fig_dir, path.basename(__file__) + f".noise.phase_vs_amp.pdf"))
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if True:
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fig, _ = figlib.fitted_histogram_figure(noise_data[:,1], fit_distr=['rice', 'rayleigh'], mean_snr=snrs['mean'])
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ax = fig.axes[0]
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ax.set_title("Noise Amplitudes")
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ax.set_xlabel("Amplitude [a.u.]")
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ax.set_ylabel("#")
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ax.legend()
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if fig_dir:
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fig.savefig(path.join(fig_dir, path.basename(__file__) + f".noise.hist_amp.pdf"))
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if show_plots:
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plt.show()
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@ -1,7 +1,8 @@
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import matplotlib.pyplot as plt
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import numpy as np
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from scipy.stats import norm
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import scipy.stats as stats
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from itertools import zip_longest
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def phase_comparison_figure(
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measured_phases,
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@ -92,3 +93,106 @@ def phase_comparison_figure(
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fig.tight_layout()
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return fig
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def fitted_histogram_figure(
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amplitudes,
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fit_distr = None,
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text_kwargs={},
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hist_kwargs={},
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mean_snr = None,
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ax = None,
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**fig_kwargs
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):
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"""
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Create a figure showing $amplitudes$ as a histogram.
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If fit_distr is a (list of) string, also fit the respective
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distribution function and show the parameters on the figure.
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"""
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default_hist_kwargs = dict(bins='sqrt', density=False, alpha=0.8, histtype='step', label='hist')
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default_text_kwargs = dict(fontsize=14, verticalalignment='top')
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if isinstance(fit_distr, str):
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fit_distr = [fit_distr]
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hist_kwargs = {**default_hist_kwargs, **hist_kwargs}
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text_kwargs = {**default_text_kwargs, **text_kwargs}
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if ax is None:
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fig, ax = plt.subplots(1,1, **fig_kwargs)
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else:
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fig = ax.get_figure()
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text_kwargs['transform'] = ax.transAxes
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counts, bins, _patches = ax.hist(amplitudes, **hist_kwargs)
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fit_info = []
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if fit_distr:
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min_x = min(amplitudes)
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max_x = max(amplitudes)
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dx = bins[1] - bins[0]
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scale = len(amplitudes) * dx
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xs = np.linspace(min_x, max_x)
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for distr in fit_distr:
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param_manipulate = lambda x: x
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if 'rice' == distr:
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name = "Rice"
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param_names = [ "$\\nu$", "$\\sigma$" ]
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distr_func = stats.rice
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fit_params2text_params = lambda x: (x[0]*x[1], x[1])
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elif 'gaussian' == distr:
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name = "Norm"
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param_names = [ "$\\mu$", "$\\sigma$" ]
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distr_func = stats.norm
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elif 'rayleigh' == distr:
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name = "Rayleigh"
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param_names = [ "$\\sigma$" ]
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distr_func = stats.rayleigh
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fit_params2text_params = lambda x: (x[0]+x[1]/2,)
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else:
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raise ValueError('Unknown distribution function '+distr)
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label = name +"(" + ','.join(param_names) + ')'
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fit_params = distr_func.fit(amplitudes)
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fit_ys = distr_func.pdf(xs, *fit_params)
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ax.plot(xs, fit_ys*scale, label=label)
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# change parameters if needed
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text_fit_params = fit_params2text_params(fit_params)
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text_str = "\n".join(
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[label]
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+
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[ f"{param} = {value: .2e}" for param, value in zip_longest(param_names, text_fit_params, fillvalue='?') ]
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)
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this_info = {
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'name': name,
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'param_names': param_names,
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'param_values': text_fit_params,
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'text_str': text_str,
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}
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fit_info.append(this_info)
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loc = (0.02, 0.95)
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ax.text(*loc, "\n\n".join([info['text_str'] for info in fit_info]), **{**text_kwargs, **dict(ha='left')})
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if mean_snr:
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text_str = f"$\\langle SNR \\rangle$ = {mean_snr: .1e}"
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loc = (0.98, 0.95)
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ax.text(*loc, text_str, **{**text_kwargs, **dict(ha='right')})
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return fig, fit_info
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