import plotstyle_plt
import numpy as np
import matplotlib.pyplot as plt
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, strip_zeros, time_bar,\
                           letter_subplot, letter_subplots
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


# GENERAL SETTINGS:
target = 'Omocestus_rufipes'
data_paths = search_files(target, excl='noise', dir='../data/inv/log_hp/')
stages = ['env', 'log', 'inv']
load_kwargs = dict(
    files=stages,
    keywords=['scales', 'measure']
)
save_path = '../figures/fig_invariance_log_hp.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(
    env=(0, slice(None)),
    log=(1, slice(None)),
    inv=(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(
    env='$x_{\\text{env}}$',
    log='$x_{\\text{dB}}$',
    inv='$x_{\\text{adapt}}$',
    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',
)
letter_snip_kwargs = dict(
    x=0.02,
    y=0.97,
    ha='left',
    va='top',
    fontsize=22,
)
letter_analysis_kwargs = dict(
    x=0,
    yref=letter_snip_kwargs['y'],
    ha='left',
    va='top',
    fontsize=22,
)
bar_time = 5
bar_kwargs = dict(
    y0=0.5,
    y1=0.6,
    color='k',
    lw = 0,
)

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

    # Load invariance data:
    pure_data, config = load_data(data_path, **load_kwargs)
    noise_data, _ = load_data(data_path.replace('.npz', '_noise.npz'), **load_kwargs)
    t_full = np.arange(pure_data['env'].shape[0]) / config['env_rate']

    # 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['env']], pure_data['example_scales']):
        ax.set_title(f'$\\alpha={strip_zeros(scale)}$')

    # 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['env']], noise_data['example_scales']):
        ax.set_title(f'$\\alpha={strip_zeros(scale)}$')
    letter_subplots([pure_subfig, noise_subfig], **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.set_xscale('symlog', linthresh=pure_data['scales'][1], linscale=0.5)
    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)
    letter_subplot(analysis_subfig, 'c', **letter_analysis_kwargs, ref=pure_subfig)

    # Plot pure-song envelope snippets:
    plot_snippets(pure_axes[snip_specs['env']], t_full, pure_data['env'],
                  ymin=0, c=colors['env'], lw=lw_snippets)

    # Plot pure-song logarithmic snippets:
    plot_snippets(pure_axes[snip_specs['log']], t_full, pure_data['log'],
                  ymax=None, c=colors['log'], lw=lw_snippets)

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

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

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

    # Plot noise-song logarithmic snippets:
    plot_snippets(noise_axes[snip_specs['log']], t_full, noise_data['log'],
                  ymax=None, c=colors['log'], lw=lw_snippets)

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

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

    # Plot pure-song SD ratios (ideal):
    base_ind = np.argmin(pure_data['scales'])
    measure_inv = pure_data['measure_inv'] / pure_data['measure_inv'][base_ind]
    analysis_ax.plot(pure_data['scales'], measure_inv, c=colors['inv'], lw=lw_analysis, ls='--')

    # Plot noise-song SD ratios (limited):
    base_ind = np.argmin(noise_data['scales'])
    measure_env = noise_data['measure_env'] / noise_data['measure_env'][base_ind]
    measure_log = noise_data['measure_log'] / noise_data['measure_log'][base_ind]
    measure_inv = noise_data['measure_inv'] / noise_data['measure_inv'][base_ind]
    analysis_ax.plot(noise_data['scales'], measure_env, c=colors['env'], lw=lw_analysis)
    analysis_ax.plot(noise_data['scales'], measure_log, c=colors['log'], lw=lw_analysis)
    analysis_ax.plot(noise_data['scales'], measure_inv, c=colors['inv'], lw=lw_analysis)
    analysis_ax.set_ylim(0.9, measure_env.max())

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

print('Done.')
embed()