Made fig_invariance_rect_lp.pdf and corresponding appendix figure.

Adjusted fig_invariance_log_hp.pdf with 2nd yaxis in dB.

Co-authored-by: Copilot <copilot@github.com>

python/fig_kernel_sd_perc_appendix.py

@@ -1,8 +1,7 @@
 import plotstyle_plt
 import numpy as np
 import matplotlib.pyplot as plt
-from thunderhopper.modeltools import load_data
-from thunderhopper.filetools import search_files, crop_paths
+from scipy.stats import norm
 from plot_functions import xlabel, ylabel
 from IPython import embed
 
@@ -26,10 +25,22 @@ fig_kwargs = dict(
     )
 )
 line_kwargs = dict(
-    color='black',
+    c='black',
     lw=1,
     alpha=0.5,
 )
+fit_kwargs = dict(
+    c='red',
+    lw=3,
+    ls='--',
+)
+grid_line_kwargs = dict(
+    visible=True,
+    which='major',
+    axis='both',
+    color='k',
+    lw=0.5,
+)
 xlab = '$\\text{multiple of }\\sigma_{k_i}$'
 ylab = '$P\\,(c_i > \\Theta_i)$'
 xlab_kwargs = dict(
@@ -50,15 +61,20 @@ data = dict(np.load(thresh_path))
 factors = data['factors']
 perc = data['percs']
 
+# Get Gaussian CDF for reference:
+fit = norm.cdf(factors, loc=0, scale=1)[::-1]
+
 # Prepare graph:
 fig, ax = plt.subplots(**fig_kwargs)
+ax.grid(**grid_line_kwargs)
 ax.set_xlim(factors[0], factors[-1])
-ax.set_ylim(0, 1)
+ax.set_ylim(-0.01, 1.01)
 ylabel(ax, ylab, transform=fig.transFigure, **ylab_kwargs)
 xlabel(ax, xlab, transform=fig.transFigure, **xlab_kwargs)
 
 # Plotting:
 ax.plot(factors, perc, **line_kwargs)
+ax.plot(factors, fit, **fit_kwargs)
 
 # Save figure:
 fig.savefig(save_path)