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 misc_functions import shorten_species, get_saturation
from color_functions import load_colors
from plot_functions import hide_axis, ylimits, super_xlabel, ylabel, hide_ticks,\
                           plot_line, strip_zeros, time_bar, zoom_inset, shift_subplot,\
                           letter_subplot, letter_subplots, title_subplot, color_axis
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[:, 1:].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)
    handles = []
    for ax, snippet in zip(axes, snippets.T):
        handles.extend(plot_line(ax, time, snippet, ymin=ymin, ymax=ymax, **kwargs))
    return handles


# GENERAL SETTINGS:
target = 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms'
data_path = search_files(target, excl='noise', dir='../data/inv/log_hp/')[0]
save_path = '../figures/fig_invariance_log_hp.pdf'
target_species = [
    'Chorthippus_biguttulus',
    'Chorthippus_mollis',
    'Chrysochraon_dispar',
    # 'Euchorthippus_declivus',
    'Gomphocerippus_rufus',
    'Omocestus_rufipes',
    'Pseudochorthippus_parallelus',
]
stages = ['env', 'log', 'inv']
load_kwargs = dict(
    files=stages,
    keywords=['scales', 'snip', 'measure']
)
relate_to_noise = True
exclude_zero = True
show_diag = True
show_plateaus = True

# GRAPH SETTINGS:
fig_kwargs = dict(
    figsize=(32/2.54, 32/2.54),
)
super_grid_kwargs = dict(
    nrows=3,
    ncols=1,
    wspace=0,
    hspace=0,
    left=0,
    right=1,
    bottom=0,
    top=1,
    height_ratios=[1, 1, 1]
)
subfig_specs = dict(
    pure=(0, slice(None)),
    noise=(1, slice(None)),
    big=(2, slice(None)),
)
block_height = 0.8
edge_padding = 0.08
pure_grid_kwargs = dict(
    nrows=len(stages),
    ncols=None,
    wspace=0.1,
    hspace=0.15,
    left=0.11,
    right=0.98,
    bottom=1 - block_height - edge_padding,
    top=1 - edge_padding,
    height_ratios=[1, 2, 1]
)
noise_grid_kwargs = dict(
    nrows=len(stages),
    ncols=None,
    wspace=pure_grid_kwargs['wspace'],
    hspace=pure_grid_kwargs['hspace'],
    left=pure_grid_kwargs['left'],
    right=pure_grid_kwargs['right'],
    bottom=edge_padding,
    top=edge_padding + block_height,
    height_ratios=[1, 2, 1]
)
big_col_shift = -0.04
big_grid_kwargs = dict(
    nrows=1,
    ncols=3,
    wspace=0.25,
    hspace=0,
    left=pure_grid_kwargs['left'] - big_col_shift,
    right=pure_grid_kwargs['right'],
    bottom=0.03,
    top=1
)
anchor_kwargs = dict(
    aspect='equal',
    adjustable='box',
    anchor=(0.5, 0.5)
)

# PLOT SETTINGS:
fs = dict(
    lab_norm=16,
    lab_tex=20,
    letter=22,
    tit_norm=16,
    tit_tex=20,
    bar=16,
)
colors = load_colors('../data/stage_colors.npz')
species_colors = load_colors('../data/species_colors.npz')
noise_colors = [(0.6,) * 3, (0.8,) * 3]
lw = dict(
    snip=1,
    big=4,
    spec=2,
    plateau=1.5,
    legend=5,
)
xlabels = dict(
    big='scale $\\alpha$',
)
ylabels = dict(
    env='$x_{\\text{env}}$\n$[\\text{a.u.}]$',
    log='$x_{\\text{log}}$\n$[\\text{dB}]$',
    inv='$x_{\\text{adapt}}$\n$[\\text{dB}]$',
    big_pure='$\\sigma_x$',
    big_noise='$\\sigma_x\\,/\\,\\sigma_{\\eta}$' if relate_to_noise else None,
)
xlab_big_kwargs = dict(
    y=0,
    fontsize=fs['lab_norm'],
    ha='center',
    va='bottom',
)
ylab_big_kwargs = dict(
    x=-0.2,
    fontsize=fs['lab_tex'],
    ha='center',
    va='bottom'
)
ylab_snip_kwargs = dict(
    x=0.03,
    fontsize=fs['lab_tex'],
    rotation=0,
    ha='center',
    va='center',
)
yloc = dict(
    env=1000,
    log=40,
    inv=20
)
title_kwargs = dict(
    x=0.5,
    y=1,
    ha='center',
    va='bottom',
    fontsize=fs['tit_norm'],
)
letter_snip_kwargs = dict(
    x=0,
    yref=0.5,
    ha='left',
    va='center',
    fontsize=fs['letter'],
)
letter_big_kwargs = dict(
    x=0,
    y=1,
    ha='left',
    va='bottom',
    fontsize=fs['letter'],
)
zoom_inset_bounds = [0.1, 0.2, 0.8, 0.6]
zoom_kwargs = dict(
    x0=0.45,
    x1=0.55,
    y0=0,
    y1=0.0006,
    low_left=True,
    low_right=True,
    ec='k',
    lw=1,
    alpha=1,
)
inset_tick_kwargs = dict(
    axis='y',
    length=3,
    pad=1,
    left=False,
    labelleft=False,
    right=True,
    labelright=True,
)
bar_time = 5
bar_kwargs = dict(
    dur=bar_time,
    y0=-0.25,
    y1=-0.1,
    xshift=1,
    color='k',
    lw=0,
    clip_on=False,
    text_pos=(-0.1, 0.5),
    text_str=f'${bar_time}\\,\\text{{s}}$',
    text_kwargs=dict(
        fontsize=fs['bar'],
        ha='right',
        va='center',
    )
)
stage_leg_kwargs = dict(
    ncols=1,
    loc='upper left',
    bbox_to_anchor=(0.05, 0.5, 0.5, 0.5),
    frameon=False,
    prop=dict(
        size=20,
    ),
    borderpad=0,
    borderaxespad=0,
    handlelength=1,
    columnspacing=1,
    handletextpad=0.5,
    labelspacing=0
)
stage_leg_labels = dict(
    env='$x_{\\text{env}}$',
    log='$x_{\\text{log}}$',
    inv='$x_{\\text{adapt}}$',
)
spec_leg_kwargs = dict(
    ncols=2,
    loc='upper right',
    bbox_to_anchor=(0, 0.6, 1, 0.4),
    frameon=False,
    prop=dict(
        size=13.5,
        style='italic',
    ),
    borderpad=0,
    borderaxespad=0,
    handlelength=0.5,
    columnspacing=1,
    handletextpad=0.5,
    labelspacing=0.25,
)
diag_kwargs = dict(
    c=(0.3,) * 3,
    lw=2,
    ls='--',
    zorder=1.9,
)
plateau_settings = dict(
    low=0.05,
    high=0.95,
    first=True,
    last=True,
    condense=None,
)
plateau_rect_kwargs = dict(
    ec='none',
    lw=0,
    zorder=1.5,
)
plateau_line_kwargs = dict(
    lw=lw['plateau'],
    ls='--',
    zorder=1,
)
plateau_dot_kwargs = dict(
    marker='o',
    markersize=8,
    markeredgewidth=1,
    clip_on=False,
)

# PREPARATION:
species_measures = {}
thresh_inds = np.zeros((len(target_species),), dtype=int)
for i, species in enumerate(target_species):
    spec_path = search_files(species, incl=['noise', 'norm-base'], dir='../data/inv/log_hp/condensed/')[0]
    spec_data = dict(np.load(spec_path))
    measure = spec_data['mean_inv'].mean(axis=-1)
    if exclude_zero:
        measure = measure[spec_data['scales'] > 0]
    species_measures[species] = measure
    thresh_inds[i] = get_saturation(measure, **plateau_settings)[1]

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

# Load invariance data:
pure_data, config = load_data(data_path, **load_kwargs)
noise_data, _ = load_data(data_path.replace('pure', 'noise'), **load_kwargs)
pure_scales, noise_scales = pure_data['scales'], noise_data['scales']
t_full = np.arange(pure_data['snip_env'].shape[0]) / config['env_rate']

if relate_to_noise:
    # Relate noise-song measures to zero scale:
    noise_data['measure_env'] /= noise_data['measure_env'][0]
    noise_data['measure_log'] /= noise_data['measure_log'][0]
    noise_data['measure_inv'] /= noise_data['measure_inv'][0]

if exclude_zero:
    # Exclude zero scales:
    inds = pure_scales > 0
    pure_scales = pure_scales[inds]
    pure_data['measure_env'] = pure_data['measure_env'][inds]
    pure_data['measure_log'] = pure_data['measure_log'][inds]
    pure_data['measure_inv'] = pure_data['measure_inv'][inds]
    inds = noise_scales > 0
    noise_scales = noise_scales[inds]
    noise_data['measure_env'] = noise_data['measure_env'][inds]
    noise_data['measure_log'] = noise_data['measure_log'][inds]
    noise_data['measure_inv'] = noise_data['measure_inv'][inds]

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

# Prepare pure-song snippet axes:
pure_grid_kwargs['ncols'] = pure_data['example_scales'].size
pure_subfig = fig.add_subfigure(super_grid[subfig_specs['pure']])
pure_axes = add_snip_axes(pure_subfig, pure_grid_kwargs)
for ax, stage in zip(pure_axes[:, 0], stages):
    ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage]))
    ylabel(ax, ylabels[stage], **ylab_snip_kwargs,
            transform=pure_subfig.transSubfigure)
for ax, scale in zip(pure_axes[0, :], pure_data['example_scales']):
    pure_title = title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', **title_kwargs)
letter_subplot(pure_subfig, 'a', ref=pure_title, **letter_snip_kwargs)
pure_inset = pure_axes[0, 0].inset_axes(zoom_inset_bounds)
pure_inset.spines[:].set(visible=True, lw=zoom_kwargs['lw'])
pure_inset.tick_params(**inset_tick_kwargs)
hide_ticks(pure_inset, 'bottom', ticks=False)

# Prepare noise-song snippet axes:
noise_grid_kwargs['ncols'] = noise_data['example_scales'].size
noise_subfig = fig.add_subfigure(super_grid[subfig_specs['noise']])
noise_axes = add_snip_axes(noise_subfig, noise_grid_kwargs)
for ax, stage in zip(noise_axes[:, 0], stages):
    ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stage]))
    ylabel(ax, ylabels[stage], **ylab_snip_kwargs,
            transform=noise_subfig.transSubfigure)
for ax, scale in zip(noise_axes[0, :], noise_data['example_scales']):
    noise_title = title_subplot(ax, f'$\\alpha={strip_zeros(scale)}$', **title_kwargs)
letter_subplot(noise_subfig, 'b', ref=noise_title, **letter_snip_kwargs)
noise_inset = noise_axes[0, 0].inset_axes(zoom_inset_bounds)
noise_inset.spines[:].set(visible=True, lw=zoom_kwargs['lw'])
noise_inset.tick_params(**inset_tick_kwargs)
hide_ticks(noise_inset, 'bottom', ticks=False)

# Prepare analysis axes:
big_subfig = fig.add_subfigure(super_grid[subfig_specs['big']])
big_grid = big_subfig.add_gridspec(**big_grid_kwargs)
big_axes = np.zeros((big_grid.ncols,), dtype=object)
for i, scales in enumerate([pure_scales, noise_scales, noise_scales]):
    ax = big_subfig.add_subplot(big_grid[0, i])
    ax.set_xlim(scales[0], scales[-1])
    ax.set_ylim(scales[0], scales[-1])
    ax.set_xscale('symlog', linthresh=scales[1], linscale=0.5)
    ax.set_yscale('symlog', linthresh=scales[1], linscale=0.5)
    # ax.xaxis.set_major_locator(plt.LogLocator(base=10, subs=[1]))
    ax.set_aspect(**anchor_kwargs)
    if i in [0, 1]:
        ax.set_ylim(scales[0], scales[-1])
        pos_equal = ax.get_position().bounds
    else:
        pos_auto = list(ax.get_position().bounds)
        ax.set_aspect('auto', adjustable='box', anchor=(0.5, 0.5))
        ax.set_position([pos_auto[0], pos_equal[1], pos_auto[2], pos_equal[3]])
        ax.set_ylim(0.9, 30)
    big_axes[i] = ax
shift_subplot(big_axes[0], dx=big_col_shift)
ylabel(big_axes[0], ylabels['big_pure'], transform=big_axes[0].transAxes, **ylab_big_kwargs)
ylabel(big_axes[1], ylabels['big_noise'], transform=big_axes[1].transAxes, **ylab_big_kwargs)
super_xlabel(xlabels['big'], big_subfig, big_axes[0], big_axes[-1], **xlab_big_kwargs)
letter_subplots(big_axes, 'cde', **letter_big_kwargs)

# Plot pure-song envelope snippets:
handle = plot_snippets(pure_axes[0, :], t_full, pure_data['snip_env'],
                        ymin=0, c=colors['env'], lw=lw['snip'])[0]
zoom_inset(pure_axes[0, 0], pure_inset, handle, transform=pure_axes[0, 0].transAxes, **zoom_kwargs)

# Plot pure-song logarithmic snippets:
plot_snippets(pure_axes[1, :], t_full, pure_data['snip_log'],
                c=colors['log'], lw=lw['snip'])

# Plot pure-song invariant snippets:
plot_snippets(pure_axes[2, :], t_full, pure_data['snip_inv'],
                c=colors['inv'], lw=lw['snip'])

# Plot noise-song envelope snippets:
ymin, ymax = pure_axes[0, 0].get_ylim()
handle = plot_snippets(noise_axes[0, :], t_full, noise_data['snip_env'],
                        ymin, ymax, c=colors['env'], lw=lw['snip'])[0]
zoom_inset(noise_axes[0, 0], noise_inset, handle, transform=noise_axes[0, 0].transAxes, **zoom_kwargs)

# Plot noise-song logarithmic snippets:
ymin, ymax = pure_axes[1, 0].get_ylim()
plot_snippets(noise_axes[1, :], t_full, noise_data['snip_log'],
                ymin, ymax, c=colors['log'], lw=lw['snip'])

# Plot noise-song invariant snippets:
ymin, ymax = pure_axes[2, 0].get_ylim()
plot_snippets(noise_axes[2, :], t_full, noise_data['snip_inv'],
                ymin, ymax, c=colors['inv'], lw=lw['snip'])

# Indicate time scale:
time_bar(noise_axes[-1, -1], **bar_kwargs)

# Plot pure-song measures (ideal):
big_axes[0].plot(pure_scales, pure_data['measure_env'], c=colors['env'], lw=lw['big'], label=stage_leg_labels['env'])
big_axes[0].plot(pure_scales, pure_data['measure_log'], c=colors['log'], lw=lw['big'], label=stage_leg_labels['log'])
big_axes[0].plot(pure_scales, pure_data['measure_inv'], c=colors['inv'], lw=lw['big'], label=stage_leg_labels['inv'])
legend = big_axes[0].legend(**stage_leg_kwargs)
[h.set_lw(lw['legend']) for h in legend.legend_handles]

# Plot noise-song measures (limited):
big_axes[1].plot(noise_scales, noise_data['measure_env'], c=colors['env'], lw=lw['big'])
big_axes[1].plot(noise_scales, noise_data['measure_log'], c=colors['log'], lw=lw['big'])
big_axes[1].plot(noise_scales, noise_data['measure_inv'], c=colors['inv'], lw=lw['big'])

if show_diag:
    # Indicate diagonal:
    big_axes[0].plot(pure_scales, pure_scales, **diag_kwargs)
    big_axes[1].plot(noise_scales, noise_scales, **diag_kwargs)

if show_plateaus:
    # Indicate low and high plateaus of noise invariance curve:
    low_ind, high_ind = get_saturation(noise_data['measure_inv'], **plateau_settings)
    big_axes[1].axvspan(noise_scales[0], noise_scales[low_ind],
                        fc=noise_colors[0], **plateau_rect_kwargs)
    big_axes[1].axvspan(noise_scales[low_ind], noise_scales[high_ind],
                        fc=noise_colors[1], **plateau_rect_kwargs)

# Plot species-specific noise-song invariance curves:
for i, (species, measure) in enumerate(species_measures.items()):
    # Plot invariance curve:
    color = species_colors[species]
    big_axes[2].plot(noise_scales, measure, label=shorten_species(species),
                        c=color, lw=lw['spec'])
    # Indicate saturation:
    ind = thresh_inds[i]
    scale = noise_scales[ind]
    big_axes[2].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
                        transform=big_axes[2].get_xaxis_transform()) 
    big_axes[2].plot(scale, 0, mfc=color, mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
                        transform=big_axes[2].get_xaxis_transform())
    big_axes[2].vlines(scale, big_axes[2].get_ylim()[0], measure[ind],
                        color=color, **plateau_line_kwargs)
legend = big_axes[2].legend(**spec_leg_kwargs)
[h.set_lw(lw['legend']) for h in legend.legend_handles]

if save_path is not None:
    fig.savefig(save_path, bbox_inches='tight')
plt.show()

print('Done.')
embed()