from pyparsing import alphanums

import plotstyle_plt
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.transforms import BboxTransformTo
from itertools import product
from thunderhopper.filetools import search_files
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 time_bar(ax, dur, y0=0.9, y1=0.95, xshift=0.5, parent=None, transform=None, **kwargs):
    t0, t1 = ax.get_xlim()
    offset = (t1 - t0 - dur) * xshift
    x0 = t0 + offset
    x1 = x0 + dur
    if parent is None:
        parent = ax
    if transform is None:
        transform = BboxTransformTo(parent.bbox)
    if transform is not ax.transData:
        trans = ax.transData + transform.inverted()
        x0 = trans.transform((x0, 0))[0]
        x1 = trans.transform((x1, 0))[0]
    parent.add_artist(plt.Rectangle((x0, y0), x1 - x0, y1 - y0,
                                    transform=transform, **kwargs))
    return None

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 = search_files(target, excl='noise', dir='../data/inv/thresh_lp/')
stages = ['conv', 'bi', 'feat']
files = stages + ['scales', 'example_scales', 'measure_conv', 'spread_conv',
                  'measure_feat', 'spread_feat']
save_path = '../figures/fig_invariance_thresh_lp.pdf'

# GRAPH SETTINGS:
fig_kwargs = dict(
    figsize=(32/2.54, 16/2.54),
)
super_grid_kwargs = dict(
    nrows=2,
    ncols=2,
    wspace=0,
    hspace=0,
    left=0,
    right=1,
    bottom=0,
    top=1
)
subfig_specs = dict(
    pure=(0, 0),
    noise=(1, 0),
    analysis=(slice(None), 1)
)
pure_grid_kwargs = dict(
    nrows=len(stages),
    ncols=None,
    wspace=0.05,
    hspace=0.1,
    left=0.13,
    right=0.95,
    bottom=0.15,
    top=0.9
)
noise_grid_kwargs = dict(
    nrows=len(stages),
    ncols=None,
    wspace=0.05,
    hspace=0.1,
    left=0.13,
    right=0.95,
    bottom=0.15,
    top=0.9   
)
analysis_grid_kwargs = dict(
    nrows=1,
    ncols=1,
    wspace=0,
    hspace=0,
    left=0.15,
    right=0.96,
    bottom=0.1,
    top=0.95
)
snip_specs = dict(
    conv=(0, slice(None)),
    bi=(1, slice(None)),
    feat=(2, slice(None))
)

# PLOT SETTINGS:
colors = load_colors('../data/stage_colors.npz')
lw_snippets = 0.5
lw_analysis = 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=0.5,
    y1=0.6,
    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:
    pure_data, config = load_data(data_path, files)
    noise_data, _ = load_data(data_path.replace('.npz', '_noise.npz'), files)
    t_full = np.arange(pure_data['conv'].shape[0]) / config['env_rate']

    # Reduce snippet data to kernel subset:
    pure_data['conv'] = pure_data['conv'][:, kernel_ind]
    pure_data['bi'] = pure_data['bi'][:, kernel_ind]
    pure_data['feat'] = pure_data['feat'][:, kernel_ind]
    noise_data['conv'] = noise_data['conv'][:, kernel_ind]
    noise_data['bi'] = noise_data['bi'][:, kernel_ind]
    noise_data['feat'] = noise_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'] = pure_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']], pure_data['example_scales']):
        ax.set_title(f'$\\alpha={strip_zeros(scale)}$')
    pure_subfig.text(s='a', **letter_snip_kwargs)

    # Prepare noise-song snippet axes:
    noise_subfig = fig.add_subfigure(super_grid[subfig_specs['noise']])
    noise_grid_kwargs['nrows' if noise_grid_kwargs['nrows'] is None else 'ncols'] = noise_data['example_scales'].size
    noise_grid = noise_subfig.add_gridspec(**noise_grid_kwargs)
    noise_axes = add_snip_axes(noise_subfig, noise_grid_kwargs)
    for ax, stage in zip(noise_axes[:, 0], stages):
        ylabel(ax, ylabels[stage], **ylab_snip_kwargs,
               transform=noise_subfig.transSubfigure)
    for ax, scale in zip(noise_axes[snip_specs['conv']], noise_data['example_scales']):
        ax.set_title(f'$\\alpha={strip_zeros(scale)}$')
    noise_subfig.text(s='b', **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(noise_data['scales'].min(), noise_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, pure_data['conv'],
                  ymin=0, c=colors['conv'], lw=lw_snippets)

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

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

    # 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'],
                  ymin=0, c=colors['conv'], lw=lw_snippets)

    # 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'],
                  c=colors['feat'], lw=lw_snippets)

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

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

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

print('Done.')
embed()