import plotstyle_plt
import glob
import numpy as np
import matplotlib.pyplot as plt
from itertools import product
from thunderhopper.modeltools import load_data
from color_functions import load_colors
from plot_functions import hide_axis, ylimits, xlabel, ylabel, plot_line, plot_barcode, strip_zeros
from IPython import embed

def add_snip_axes(fig, grid_kwargs):
    grid = fig.add_gridspec(**grid_kwargs)
    axes = np.zeros((grid.nrows, grid.ncols), dtype=object)
    for i, j in product(range(grid.nrows), range(grid.ncols)):
        axes[i, j] = fig.add_subplot(grid[i, j])
    [hide_axis(ax, 'left') for ax in axes.flatten()]
    [hide_axis(ax, 'bottom') for ax in axes.flatten()]
    return axes

def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
    ymin, ymax = ylimits(snippets, minval=ymin, maxval=ymax, pad=0.05)
    for ax, snippet in zip(axes, snippets.T):
        plot_line(ax, time, snippet, ymin=ymin, ymax=ymax, **kwargs)
    return None

def plot_bi_snippets(axes, time, binary, **kwargs):
    for ax, binary in zip(axes, binary.T):
        plot_barcode(ax, time, binary[:, None], **kwargs)
    return None


# GENERAL SETTINGS:
target = 'Omocestus_rufipes'
data_paths = glob.glob(f'../data/processed/{target}*.npz')
stages = ['filt', 'env', 'log', 'inv', 'conv', 'bi', 'feat']
load_kwargs = dict(
    files=stages,
    keywords=['scales', 'measure', 'spread']
)
save_path = None#'../figures/fig_invariance_full.pdf'

# GRAPH SETTINGS:
fig_kwargs = dict(
    figsize=(32/2.54, 16/2.54),
)
super_grid_kwargs = dict(
    nrows=len(stages),
    ncols=3,
    wspace=0,
    hspace=0,
    left=0,
    right=1,
    bottom=0,
    top=1
)
subfig_specs = dict(
    **{stage: (slice(0, -1), i) for i, stage in enumerate(stages)},
    big=(slice(None), -1)
)
stage_grid_kwargs = dict(
    nrows=1,
    ncols=None,
    wspace=0.05,
    hspace=0,
    left=0.07,
    right=0.95,
    bottom=0.15,
    top=0.9
)
big_grid_kwargs = dict(
    nrows=1,
    ncols=1,
    wspace=0,
    hspace=0,
    left=0.15,
    right=0.96,
    bottom=0.1,
    top=0.95
)

# PLOT SETTINGS:
colors = load_colors('../data/stage_colors.npz')
lw_snippets = dict(
    raw=0.25,
    filt=0.25,
    env=0.5,
    log=0.5,
    inv=0.5,
    conv=0.5,
    bi=0.01,
    feat=2
)
lw_big = 3
xlabels = dict(
    analysis='scale $\\alpha$',
)
xlab_analysis_kwargs = dict(
    y=0.01,
    fontsize=16,
    ha='center',
    va='bottom',
)
ylabels = dict(
    conv='$c_i$',
    bi='$b_i$',
    feat='$f_i$',
    analysis='ratio $\\text{SD}_{\\alpha}\\,/\\,\\text{SD}_{\\min[\\alpha]}$',
    # analysis='ratio $\\sigma_{\\alpha}\\,/\\,\\sigma_{\\min[\\alpha]}$',
)
ylab_snip_kwargs = dict(
    x=0.01,
    fontsize=20,
    rotation=0,
    ha='left',
    va='center',
)
ylab_analysis_kwargs = dict(
    x=0.02,
    fontsize=16,
    ha='center',
    va='top',
)
# xloc = dict(
#     analysis=10,
# )
# letter_snip_kwargs = dict(
#     x=0.02,
#     y=1,
#     ha='left',
#     va='top',
#     fontsize=22,
#     fontweight='bold'
# )
# letter_analysis_kwargs = dict(
#     x=0,
#     y=1,
#     ha='left',
#     va='top',
#     fontsize=22,
#     fontweight='bold'
# )
# bar_time = 5
# bar_kwargs = dict(
#     y0=0.7,
#     y1=0.8,
#     color='k',
#     lw=0,
# )
# spread_kwargs = dict(
#     alpha=0.3,
#     lw=0,
#     zorder=0
# )
# kernel_ind = 0

# EXECUTION:
for data_path in data_paths:
    print(f'Processing {data_path}')

    # Load invariance data:
    data, config = load_data(data_path, **load_kwargs)
    t_full = np.arange(data['conv'].shape[0]) / config['env_rate']

    # Reduce snippet data to kernel subset:
    data['conv'] = data['conv'][:, kernel_ind]
    data['bi'] = data['bi'][:, kernel_ind]
    data['feat'] = data['feat'][:, kernel_ind]

    # Prepare overall graph:
    fig = plt.figure(**fig_kwargs)
    super_grid = fig.add_gridspec(**super_grid_kwargs)

    # Prepare pure-song snippet axes:
    pure_subfig = fig.add_subfigure(super_grid[subfig_specs['pure']])
    pure_grid_kwargs['nrows' if pure_grid_kwargs['nrows'] is None else 'ncols'] = data['example_scales'].size
    pure_axes = add_snip_axes(pure_subfig, pure_grid_kwargs)
    for ax, stage in zip(pure_axes[:, 0], stages):
        ylabel(ax, ylabels[stage], **ylab_snip_kwargs,
               transform=pure_subfig.transSubfigure)
    for ax, scale in zip(pure_axes[snip_specs['conv']], data['example_scales']):
        ax.set_title(f'$\\alpha={strip_zeros(scale)}$')
    pure_subfig.text(s='a', **letter_snip_kwargs)

    # Prepare analysis axis:
    analysis_subfig = fig.add_subfigure(super_grid[subfig_specs['analysis']])
    analysis_grid = analysis_subfig.add_gridspec(**analysis_grid_kwargs)
    analysis_ax = analysis_subfig.add_subplot(analysis_grid[0, 0])
    analysis_ax.set_xlim(data['scales'].min(), data['scales'].max())
    analysis_ax.xaxis.set_major_locator(plt.MultipleLocator(xloc['analysis']))
    xlabel(analysis_ax, xlabels['analysis'], **xlab_analysis_kwargs,
           transform=analysis_subfig.transSubfigure)
    # analysis_ax.set_yscale('log')
    ylabel(analysis_ax, ylabels['analysis'], **ylab_analysis_kwargs,
           transform=analysis_subfig.transSubfigure)
    analysis_subfig.text(s='c', **letter_analysis_kwargs)

    # Plot pure-song kernel response snippets:
    plot_snippets(pure_axes[snip_specs['conv']], t_full, data['conv'],
                  c=colors['conv'], lw=lw_snippets['conv'])

    # Plot pure-song binary snippets:
    plot_bi_snippets(pure_axes[snip_specs['bi']], t_full, data['bi'],
                     color=colors['bi'], lw=0)

    # Plot pure-song feature snippets:
    plot_snippets(pure_axes[snip_specs['feat']], t_full, data['feat'],
                  ymin=0, ymax=1, c=colors['feat'], lw=lw_snippets['feat'])

    # Indicate time scale:
    time_bar(pure_axes[snip_specs['conv']][0], bar_time, **bar_kwargs)

    # # Plot noise-song kernel response snippets:
    # plot_snippets(noise_axes[snip_specs['conv']], t_full, noise_data['conv'],
    #               c=colors['conv'], lw=lw_snippets['conv'])

    # # Plot noise-song binary snippets:
    # plot_bi_snippets(noise_axes[snip_specs['bi']], t_full, noise_data['bi'],
    #                  color=colors['bi'], lw=0)

    # # Plot noise-song feature snippets:
    # plot_snippets(noise_axes[snip_specs['feat']], t_full, noise_data['feat'],
    #               ymin=0, ymax=1, c=colors['feat'], lw=lw_snippets['feat'])

    # # Indicate time scale:
    # time_bar(noise_axes[snip_specs['conv']][0], bar_time, **bar_kwargs)

    # Plot noise-song SD ratios (limited):
    analysis_ax.plot(data['scales'], data['measure_conv'],
                    c=colors['conv'], lw=lw_analysis)
    lower, upper = data['spread_conv']
    analysis_ax.fill_between(data['scales'], lower, upper,
                             color=colors['conv'], **spread_kwargs)
    analysis_ax.plot(data['scales'], data['measure_feat'],
                    c=colors['feat'], lw=lw_analysis)
    lower, upper = data['spread_feat']
    analysis_ax.fill_between(data['scales'], lower, upper,
                             color=colors['feat'], **spread_kwargs)

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

print('Done.')
embed()