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 plot_functions import xlabel, ylabel
from IPython import embed

# Analysis settings:
mode = ['thresh_lp', 'full', 'short', 'field'][3]
thresh_path = f'../data/inv/{mode}/thresholds.npz'
save_path = f'../figures/fig_kernel_sd_perc_{mode}_appendix.pdf'

# Plot settings:
fig_kwargs = dict(
    figsize=(32/2.54, 16/2.54),
    nrows=1,
    ncols=1,
    gridspec_kw=dict(
        wspace=0,
        hspace=0,
        left=0.09,
        right=0.99,
        bottom=0.11,
        top=0.98,
    )
)
line_kwargs = dict(
    color='black',
    lw=1,
    alpha=0.5,
)
xlab = '$\\text{multiple of }\\sigma_{k_i}$'
ylab = '$P\\,(c_i > \\Theta_i)$'
xlab_kwargs = dict(
    y=0,
    fontsize=20,
    ha='center',
    va='bottom',
)
ylab_kwargs = dict(
    x=0,
    fontsize=20,
    ha='center',
    va='top',
)

# Load threshold data:
data = dict(np.load(thresh_path))
factors = data['factors']
perc = data['percs']

# Prepare graph:
fig, ax = plt.subplots(**fig_kwargs)
ax.set_xlim(factors[0], factors[-1])
ax.set_ylim(0, 1)
ylabel(ax, ylab, transform=fig.transFigure, **ylab_kwargs)
xlabel(ax, xlab, transform=fig.transFigure, **xlab_kwargs)

# Plotting:
ax.plot(factors, perc, **line_kwargs)

# Save figure:
fig.savefig(save_path)

plt.show()
print('Done.')