import plotstyle_plt
import numpy as np
import matplotlib.pyplot as plt
from plot_functions import xlabel, ylabel

# GENERAL SETTINGS:
data_path = '../data/inv/noise_env/sd_conversion.npz'
save_path = '../figures/fig_noise_env_sd_conversion_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=0.08,
        right=0.98,
        bottom=0.1,
        top=0.95,
    )
)
grid_line_kwargs = dict(
    visible=True,
    which='major',
    axis='both',
    color='k',
    lw=0.5,
)
line_kwargs = dict(
    c='k',
    lw=0.5,
    alpha=0.5,
)
xlab = '$\\text{scale }\\alpha$'
xlab_kwargs = dict(
    y=0,
    fontsize=20,
    ha='center',
    va='bottom',
)
ylab = '$\\sigma_{\\eta}$'
ylab_kwargs = dict(
    x=0,
    fontsize=20,
    ha='center',
    va='top',
)

# Fetch data:
data = dict(np.load('../data/inv/noise_env/sd_conversion.npz'))

# Prepare graph:
fig, ax = plt.subplots(**fig_kwargs)
ax.grid(**grid_line_kwargs)
ax.set_xlim(data['scales'][0], data['scales'][-1])
ax.set_xscale('symlog', linthresh=data['scales'][1], linscale=0.5)
ax.set_ylim(0, 0.08)
ax.yaxis.set_major_locator(plt.MultipleLocator(0.02))
xlabel(ax, xlab, transform=fig.transFigure, **xlab_kwargs)
ylabel(ax, ylab, transform=fig.transFigure, **ylab_kwargs)

# Plot individual trials:
ax.plot(data['scales'], data['sd_noise'][..., 0], **line_kwargs)

if save_path is not None:
    fig.savefig(save_path)
plt.show()