7e1aa8721a
Adjusted fig_invariance_log_hp.pdf with 2nd yaxis in dB. Co-authored-by: Copilot <copilot@github.com>
86 lines
1.6 KiB
Python
86 lines
1.6 KiB
Python
import plotstyle_plt
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import numpy as np
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import matplotlib.pyplot as plt
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from scipy.stats import norm
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from plot_functions import xlabel, ylabel
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from IPython import embed
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# Analysis settings:
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mode = ['thresh_lp', 'full', 'short', 'field'][3]
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thresh_path = f'../data/inv/{mode}/thresholds.npz'
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save_path = f'../figures/fig_kernel_sd_perc_{mode}_appendix.pdf'
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# Plot settings:
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fig_kwargs = dict(
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figsize=(32/2.54, 16/2.54),
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nrows=1,
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ncols=1,
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gridspec_kw=dict(
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wspace=0,
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hspace=0,
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left=0.09,
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right=0.99,
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bottom=0.11,
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top=0.98,
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)
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)
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line_kwargs = dict(
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c='black',
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lw=1,
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alpha=0.5,
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)
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fit_kwargs = dict(
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c='red',
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lw=3,
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ls='--',
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)
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grid_line_kwargs = dict(
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visible=True,
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which='major',
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axis='both',
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color='k',
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lw=0.5,
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)
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xlab = '$\\text{multiple of }\\sigma_{k_i}$'
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ylab = '$P\\,(c_i > \\Theta_i)$'
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xlab_kwargs = dict(
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y=0,
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fontsize=20,
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ha='center',
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va='bottom',
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)
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ylab_kwargs = dict(
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x=0,
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fontsize=20,
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ha='center',
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va='top',
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)
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# Load threshold data:
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data = dict(np.load(thresh_path))
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factors = data['factors']
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perc = data['percs']
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# Get Gaussian CDF for reference:
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fit = norm.cdf(factors, loc=0, scale=1)[::-1]
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# Prepare graph:
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fig, ax = plt.subplots(**fig_kwargs)
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ax.grid(**grid_line_kwargs)
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ax.set_xlim(factors[0], factors[-1])
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ax.set_ylim(-0.01, 1.01)
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ylabel(ax, ylab, transform=fig.transFigure, **ylab_kwargs)
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xlabel(ax, xlab, transform=fig.transFigure, **xlab_kwargs)
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# Plotting:
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ax.plot(factors, perc, **line_kwargs)
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ax.plot(factors, fit, **fit_kwargs)
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# Save figure:
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fig.savefig(save_path)
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plt.show()
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print('Done.')
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