import plotstyle_plt
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm
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(
    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(
    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']

# 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(-0.01, 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)

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