mirror of
https://gitlab.science.ru.nl/mthesis-edeboone/m-thesis-introduction.git
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287 lines
8.9 KiB
Python
287 lines
8.9 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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from scipy import stats
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from scipy import special
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from scipy import optimize
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from itertools import zip_longest
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def expectation(x,pdfx):
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dx = x[1]-x[0]
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return np.sum(x*pdfx*dx)
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def variance(x,pdfx):
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mu = expectation(x,pdfx)
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dx = x[1]-x[0]
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return np.sum((x**2*pdfx*dx))-mu**2
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def random_phase_sum_distribution(theta, sigma, s=1):
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theta = np.asarray(theta)
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ct = np.cos(theta)
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st = np.sin(theta)
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k = s/sigma
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pipi = 2*np.pi
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return (np.exp(-k**2/2)/pipi) + (
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(pipi**-0.5)*k*np.exp(-(k*st)**2/2)) * (
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(1.+special.erf(k*ct*2**-0.5))*ct/2)
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def gaussian_phase_distribution(theta, sigma, s=1):
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theta = np.asarray(theta)
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k=s/sigma
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return (2*np.pi)**-0.5*k*np.exp(-(k*theta)**2/2)
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def phase_comparison_figure(
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measured_phases,
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true_phases,
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plot_residuals=True,
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f_beacon=None,
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hist_kwargs={},
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sc_kwargs={},
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text_kwargs={},
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colors=['blue', 'orange'],
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legend_on_scatter=True,
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secondary_axis='time',
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fit_gaussian=False,
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fit_randomphasesum=False,
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mean_snr=None,
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return_fit_info=False,
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**fig_kwargs
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):
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"""
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Create a figure comparing measured_phase against true_phase
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by both plotting the values, and the residuals.
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"""
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default_fig_kwargs = dict(sharex=True)
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default_hist_kwargs = dict(bins='sqrt', density=False, alpha=0.8, histtype='step')
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default_text_kwargs = dict(fontsize=14, verticalalignment='top')
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default_sc_kwargs = dict(alpha=0.6, ls='none')
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do_hist_plot = hist_kwargs is not False
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if hist_kwargs is False:
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hist_kwargs = {}
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do_scatter_plot = sc_kwargs is not False
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if sc_kwargs is False:
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sc_kwargs = {}
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fig_kwargs = {**default_fig_kwargs, **fig_kwargs}
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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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sc_kwargs = {**default_sc_kwargs, **sc_kwargs}
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fig, axs = plt.subplots(0+do_hist_plot+do_scatter_plot, 1, **fig_kwargs)
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if not hasattr(axs, '__len__'):
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axs = [axs]
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if f_beacon and secondary_axis in ['phase', 'time']:
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phase2time = lambda x: x/(2*np.pi*f_beacon)
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time2phase = lambda x: 2*np.pi*x*f_beacon
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if secondary_axis == 'time':
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functions = (phase2time, time2phase)
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label = 'Time $\\varphi/(2\\pi f_{beac})$ [ns]'
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else:
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functions = (time2phase, phase2time)
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label = 'Phase $2\\pi t f_{beac}$ [rad]'
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secax = axs[0].secondary_xaxis('top', functions=functions)
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# Histogram
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fit_info = {}
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if do_hist_plot:
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i=0
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axs[i].set_ylabel("#")
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this_kwargs = dict(
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ax = axs[i],
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text_kwargs=text_kwargs,
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hist_kwargs={**hist_kwargs, **dict(label='Measured', color=colors[0], ls='solid')},
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mean_snr=mean_snr,
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fit_distr=[],
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)
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if fit_gaussian:
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this_kwargs['fit_distr'].append('gaussian')
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if fit_randomphasesum:
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this_kwargs['fit_distr'].append('randomphasesum')
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_, fit_info = fitted_histogram_figure(
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measured_phases,
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**this_kwargs
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)
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if not plot_residuals: # also plot the true clock phases
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_bins = fit_info['bins']
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axs[i].hist(true_phases, color=colors[1], label='Actual', ls='dashed', **{**hist_kwargs, **dict(bins=_bins)})
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# Scatter plot
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if do_scatter_plot:
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i=1
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axs[i].set_ylabel("Antenna no.")
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axs[i].plot(measured_phases, np.arange(len(measured_phases)), marker='x' if plot_residuals else '3', color=colors[0], label='Measured', **sc_kwargs)
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if not plot_residuals: # also plot the true clock phases
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axs[i].plot(true_phases, np.arange(len(true_phases)), marker='4', color=colors[1], label='Actual', **sc_kwargs)
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if not plot_residuals and legend_on_scatter:
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axs[i].legend()
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fig.tight_layout()
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if return_fit_info:
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return fig, fit_info
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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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calc_chisq = True,
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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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bin_centers = bins[:-1] + np.diff(bins) / 2
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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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fit_params2text_params = lambda x: x
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fit_ys = None
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fit_params = None
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cdf = None
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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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elif 'randomphasesum' == distr:
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name = "RandPhaseS"
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param_names = [ "$\\sigma$", 's']
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pdf = random_phase_sum_distribution
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bounds = ((0,0.9999), (np.inf,1))
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fit_params, pcov = optimize.curve_fit(pdf, bin_centers, counts, bounds=bounds)
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fit_ys = pdf( xs, *fit_params)
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fit_params2text_params = lambda x: (x[1], x[0])
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elif 'gaussphase' == distr:
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name = 'GaussPhase'
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param_names = [ "$\\sigma$", 's']
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pdf = gaussian_phase_distribution
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bounds = ((0,0.9999), (np.inf,1))
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fit_params, pcov = optimize.curve_fit(pdf, bin_centers, counts, bounds=bounds)
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fit_ys = pdf( xs, *fit_params)
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fit_params2text_params = lambda x: (x[1], x[0])
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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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if fit_ys is None:
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fit_params = distr_func.fit(amplitudes)
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fit_ys = scale * distr_func.pdf(xs, *fit_params)
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cdf = distr_func.cdf
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ax.plot(xs, fit_ys, label=label)
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chisq_strs = []
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if calc_chisq and cdf:
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ct = np.diff(cdf(bins, *fit_params))*np.sum(counts)
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if True: # stabilise the chisquare derivation
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ct *= np.sum(counts)/np.sum(ct)
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c2t = stats.chisquare(counts, ct, ddof=len(fit_params))
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chisq_strs = [
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f"$\\chi^2$/dof = {c2t[0]: .2g}/{len(fit_params)}"
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]
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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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chisq_strs
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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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if chisq_strs:
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this_info['chisq'] = c2t[0]
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this_info['dof'] = len(fit_params)
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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, dict(fit_info=fit_info, counts=counts, bins=bins)
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