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 thunderhopper.filtertools import find_kern_specs
from misc_functions import get_saturation, reduce_kernel_set, exclude_zero_scale,\
                            divide_by_zero, x_dist, y_dist
from color_functions import load_colors
from plot_functions import hide_axis, reorder_by_sd, ylimits, super_xlabel, ylabel, title_subplot,\
                           plot_line, strip_zeros, time_bar, assign_colors,\
                           letter_subplot, letter_subplots, hide_ticks
from IPython import embed

def plot_snippets(axes, time, snippets, ymin=None, ymax=None, **kwargs):
    ymin, ymax = ylimits(snippets, minval=ymin, maxval=ymax, pad=0.05)
    handles = []
    for i, ax in enumerate(axes):
        handles.append(plot_line(ax, time, snippets[:, ..., i],
                       ymin=ymin, ymax=ymax, **kwargs))
    return handles

def plot_curves(ax, scales, measures, **kwargs):
    if measures.ndim == 1:
        handles = ax.plot(scales, measures, **kwargs)
        return handles, measures
    median_measure = np.nanmedian(measures, axis=1)
    line_handle = ax.plot(scales, median_measure, **kwargs)[0]
    return line_handle, median_measure

def crop_noise_snippets(snippets, nin, nout, stages=['filt', 'env', 'log', 'inv', 'conv', 'feat']):
    half_offset = int((nin - nout) / 2)
    segment = np.arange(half_offset, half_offset + nout)
    for stage in stages:
        key = f'snip_{stage}'
        snippets[key] = snippets[key][segment, ...]
    return snippets


# GENERAL SETTINGS:
search_target = 'Pseudochorthippus_parallelus'
stages = ['filt', 'env', 'log', 'inv', 'conv', 'feat']
song_example = 'Pseudochorthippus_parallelus_micarray-short_JJ_20240815T160355-20240815T160755-1m10s690ms-1m13s614ms'
noise_example = 'merged_noise'
song_path = '../data/inv/field/song/'
noise_path = '../data/inv/field/noise/'
raw_path = search_files(search_target, incl='unnormed', dir=song_path + 'condensed/')[0]
base_path = search_files(search_target, incl='base', dir=song_path + 'condensed/')[0]
range_path = search_files(search_target, incl='range', dir=song_path + 'condensed/')[0] 
song_snip_path = search_files(song_example, dir=song_path)[0]
noise_snip_path = search_files(noise_example, dir=noise_path)[0]
save_path = '../figures/fig_invariance_field.pdf'

# ANALYSIS SETTINGS:
offset_distance = 10 # centimeter

# SUBSET SETTINGS:
types = np.array([1, -1, 2, -2, 3, -3, 4, -4])
# types = [1, -1, 2, -2, 3, -3, 4, -4, 5, -5, 6, -6, 7, -7, 8, -8, 9, -9, 10, -10]
sigmas = np.array([0.001, 0.002, 0.004, 0.008, 0.016])
# sigmas = [0.001, 0.002, 0.004, 0.008, 0.016, 0.032]
kernels = None
reduce_kernels = any(var is not None for var in [kernels, types, sigmas])

# GRAPH SETTINGS:
fig_kwargs = dict(
    figsize=(32/2.54, 32/2.54),
)
super_grid_kwargs = dict(
    nrows=2,
    ncols=1,
    wspace=0,
    hspace=0,
    left=0,
    right=1,
    bottom=0,
    top=1,
    height_ratios=[3, 2]
)
subfig_specs = dict(
    snip=(0, 0),
    big=(1, 0),
)
snip_grid_kwargs = dict(
    nrows=len(stages),
    ncols=None,
    wspace=0.1,
    hspace=0.4,
    left=0.11,
    right=0.98,
    bottom=0.08,
    top=0.95
)
big_grid_kwargs = dict(
    nrows=1,
    ncols=3,
    wspace=0.4,
    hspace=0,
    left=snip_grid_kwargs['left'],
    right=snip_grid_kwargs['right'],
    bottom=0.13,
    top=0.98
)

# 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')
conv_colors = load_colors('../data/conv_colors_all.npz')
feat_colors = load_colors('../data/feat_colors_all.npz')
lw = dict(
    filt=0.25,
    env=0.25,
    log=0.25,
    inv=0.25,
    conv=0.25,
    feat=1,
    big=3,
    plateau=1.5,
)
xlabels = dict(
    big='distance [cm]',
)
ylabels = dict(
    filt='$x_{\\text{filt}}$\n$[\\text{a.u.}]$',
    env='$x_{\\text{env}}$\n$[\\text{a.u.}]$',
    log='$x_{\\text{log}}$\n$[\\text{dB}]$',
    inv='$x_{\\text{adapt}}$\n$[\\text{dB}]$',
    conv='$c_i$\n$[\\text{dB}]$',
    feat='$f_i$',
    big=['measure', 'rel. measure', 'norm. measure']
)
xlab_big_kwargs = dict(
    y=0,
    fontsize=fs['lab_norm'],
    ha='center',
    va='bottom',
)
ylab_snip_kwargs = dict(
    x=0,
    fontsize=fs['lab_tex'],
    rotation=0,
    ha='left',
    va='center'
)
ylab_big_kwargs = dict(
    x=-0.2,
    fontsize=fs['lab_norm'],
    ha='center',
    va='bottom',
)
yloc = dict(
    filt=0.03,
    env=0.01,
    log=50,
    inv=20,
    conv=1,
    feat=1,
)
title_kwargs = dict(
    x=0.5,
    yref=1,
    ha='center',
    va='top',
    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'],
)
song_bar_time = 1
song_bar_kwargs = dict(
    dur=song_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'${song_bar_time}\\,\\text{{s}}$',
    text_kwargs=dict(
        fontsize=fs['bar'],
        ha='right',
        va='center',
    )
)
noise_bar_time = 0.5
noise_bar_kwargs = song_bar_kwargs.copy()
noise_bar_kwargs['dur'] = noise_bar_time
noise_bar_kwargs['text_str'] = f'${int(1000 * noise_bar_time)}\\,\\text{{ms}}$'
plateau_settings = dict(
    low=0.05,
    high=0.95,
    first=True,
    last=True,
    condense=None,
)
plateau_line_kwargs = dict(
    lw=lw['plateau'],
    ls='--',
    zorder=1,
)
plateau_dot_kwargs = dict(
    marker='o',
    markersize=8,
    markeredgewidth=1,
    clip_on=False,
)

# EXECUTION:

# Load raw (unnormed) invariance data:
data, config = load_data(raw_path, files='distances', keywords='mean')
dists = data['distances'] + offset_distance

# Load snippet data:
song_snip, _ = load_data(song_snip_path, keywords='snip')
t_song = np.arange(song_snip['snip_filt'].shape[0]) / config['rate']
noise_snip, _ = load_data(noise_snip_path, keywords='snip')
noise_snip = crop_noise_snippets(noise_snip, noise_snip['snip_filt'].shape[0], t_song.size)
t_noise = np.arange(noise_snip['snip_filt'].shape[0]) / config['rate']
snip_dists = ['noise'] + [f'{int(d)}$\\,$cm' for d in dists]

# Optional kernel subset:
reduce_kernels = False
if any(var is not None for var in [kernels, types, sigmas]):
    kern_inds = find_kern_specs(config['k_specs'], kernels, types, sigmas)
    data = reduce_kernel_set(data, kern_inds, keyword='mean')
    song_snip = reduce_kernel_set(song_snip, kern_inds, keyword='snip')
    noise_snip = reduce_kernel_set(noise_snip, kern_inds, keyword='snip')
    config['k_specs'] = config['k_specs'][kern_inds, :]
    config['kernels'] = config['kernels'][:, kern_inds]
    reduce_kernels = True

# Adjust grid parameters:
snip_grid_kwargs['ncols'] = len(snip_dists)

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

# Prepare stage-specific snippet axes:
snip_subfig = fig.add_subfigure(super_grid[subfig_specs['snip']])
snip_grid = snip_subfig.add_gridspec(**snip_grid_kwargs)
snip_axes = np.zeros((snip_grid.nrows, snip_grid.ncols), dtype=object)
for i, j in product(range(snip_grid.nrows), range(snip_grid.ncols)):
    ax = snip_subfig.add_subplot(snip_grid[i, j])
    ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stages[i]]))
    hide_axis(ax, 'bottom')
    if i == 0:
        title = title_subplot(ax, snip_dists[j], ref=snip_subfig, **title_kwargs)
    if j == 0:
        ax.set_xlim(t_noise[0], t_noise[-1])
        ylabel(ax, ylabels[stages[i]], **ylab_snip_kwargs, transform=snip_subfig.transSubfigure)
    else:
        ax.set_xlim(t_song[0], t_song[-1])
        hide_axis(ax, 'left')
    snip_axes[i, j] = ax
time_bar(snip_axes[-1, -1], **song_bar_kwargs)
# time_bar(snip_axes[-1, 0], **noise_bar_kwargs)
letter_subplot(snip_subfig, 'a', ref=title, **letter_snip_kwargs)

# 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 in range(big_grid.ncols):
    ax = big_subfig.add_subplot(big_grid[0, i])
    ax.set_xlim(dists[0], 0)
    # ax.set_xscale('symlog', linthresh=offset_distance, linscale=0.5)
    ax.set_yscale('symlog', linthresh=0.01, linscale=0.1)
    ylabel(ax, ylabels['big'][i], **ylab_big_kwargs)
    # if i < (big_grid.ncols - 1):
    #     ax.set_ylim(scales[0], scales[-1])
    # else:
    #     ax.set_ylim(0, 1)
    big_axes[i] = ax
super_xlabel(xlabels['big'], big_subfig, big_axes[0], big_axes[-1], **xlab_big_kwargs)
letter_subplots(big_axes, 'bcd', **letter_big_kwargs)

if True:
    # Plot filtered snippets:
    plot_snippets(snip_axes[0, 1:], t_song, song_snip['snip_filt'],
                  c=colors['filt'], lw=lw['filt'])
    plot_line(snip_axes[0, 0], t_noise, noise_snip['snip_filt'][:, 0],
              *snip_axes[0, 1].get_ylim(), c=colors['filt'], lw=lw['filt'])

    # Plot envelope snippets:
    plot_snippets(snip_axes[1, 1:], t_song, song_snip['snip_env'],
                  ymin=0, c=colors['env'], lw=lw['env'])
    plot_line(snip_axes[1, 0], t_noise, noise_snip['snip_env'][:, 0],
              *snip_axes[1, 1].get_ylim(), c=colors['env'], lw=lw['env'])

    # Plot logarithmic snippets:
    plot_snippets(snip_axes[2, 1:], t_song, song_snip['snip_log'],
                  c=colors['log'], lw=lw['log'])
    plot_line(snip_axes[2, 0], t_noise, noise_snip['snip_log'][:, 0],
              *snip_axes[2, 1].get_ylim(), c=colors['log'], lw=lw['log'])

    # Plot invariant snippets:
    plot_snippets(snip_axes[3, 1:], t_song, song_snip['snip_inv'],
                  c=colors['inv'], lw=lw['inv'])
    plot_line(snip_axes[3, 0], t_noise, noise_snip['snip_inv'][:, 0],
              *snip_axes[3, 1].get_ylim(), c=colors['inv'], lw=lw['inv'])

    # Plot kernel response snippets:
    all_handles = plot_snippets(snip_axes[4, 1:], t_song, song_snip['snip_conv'],
                                 c=colors['conv'], lw=lw['conv'])
    for i, handles in enumerate(all_handles):
        assign_colors(handles, config['k_specs'][:, 0], conv_colors)
        reorder_by_sd(handles, song_snip['snip_conv'][..., i])
    handles = plot_line(snip_axes[4, 0], t_noise, noise_snip['snip_conv'][:, 0],
                        *snip_axes[4, 1].get_ylim(), c=colors['conv'], lw=lw['conv'])
    assign_colors(handles, config['k_specs'][:, 0], conv_colors)
    reorder_by_sd(handles, noise_snip['snip_conv'][:, 0])

    # Plot feature snippets:
    all_handles = plot_snippets(snip_axes[5, 1:], t_song, song_snip['snip_feat'],
                                ymin=0, ymax=1, c=colors['feat'], lw=lw['feat'])
    for i, handles in enumerate(all_handles):
        assign_colors(handles, config['k_specs'][:, 0], feat_colors)
        reorder_by_sd(handles, song_snip['snip_feat'][..., i])
    handles = plot_line(snip_axes[5, 0], t_noise, noise_snip['snip_feat'][:, 0],
                        ymin=0, ymax=1, c=colors['feat'], lw=lw['feat'])
    assign_colors(handles, config['k_specs'][:, 0], feat_colors)
    reorder_by_sd(handles, noise_snip['snip_feat'][:, 0])
del song_snip, noise_snip

# Remember saturation points:
crit_inds, crit_dists = {}, {}

# Unnormed measures:
for stage in stages:
    # Plot average intensity measure across recordings:
    curve = plot_curves(big_axes[0], dists, data[f'mean_{stage}'],
                        c=colors[stage], lw=lw['big'],
                        fill_kwargs=dict(color=colors[stage], alpha=0.25))
    # # Indicate saturation point:
    # if stage in ['log', 'inv', 'conv', 'feat']:
    #     ind = get_saturation(curve, **plateau_settings)[1]
    #     dist = dists[ind]
    #     big_axes[0].plot(dist, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
    #                      transform=big_axes[0].get_xaxis_transform()) 
    #     big_axes[0].plot(dist, 0, mfc=colors[stage], mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
    #                      transform=big_axes[0].get_xaxis_transform())
    #     big_axes[0].vlines(dist, big_axes[0].get_ylim()[0], curve[ind],
    #                        color=colors[stage], **plateau_line_kwargs)
    #     # Log saturation point:
    #     crit_inds[stage] = ind
    #     crit_dists[stage] = dist
del data

# Noise baseline-related measures:
data, _ = load_data(base_path, files='scales', keywords='mean')
if reduce_kernels:
    data = reduce_kernel_set(data, kern_inds, keyword='mean')
for stage in stages:
    # Plot average intensity measure across recordings:
    curve = plot_curves(big_axes[1], dists, data[f'mean_{stage}'],
                        c=colors[stage], lw=lw['big'],
                        fill_kwargs=dict(color=colors[stage], alpha=0.25))
    # Indicate saturation point:
    # if stage in ['log', 'inv', 'conv', 'feat']:
    #     ind, dist = crit_inds[stage], crit_dists[stage]
    #     big_axes[1].plot(dist, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
    #                      transform=big_axes[1].get_xaxis_transform()) 
    #     big_axes[1].plot(dist, 0, mfc=colors[stage], mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
    #                      transform=big_axes[1].get_xaxis_transform())
    #     big_axes[1].vlines(dist, big_axes[1].get_ylim()[0], curve[ind],
    #                        color=colors[stage], **plateau_line_kwargs)
del data

# Min-max normalized measures:
data, _ = load_data(range_path, files='scales', keywords='mean')
if reduce_kernels:
    data = reduce_kernel_set(data, kern_inds, keyword='mean')
for stage in stages:
    # Plot average intensity measure across recordings:
    curve = plot_curves(big_axes[2], dists, data[f'mean_{stage}'],
                        c=colors[stage], lw=lw['big'],
                        fill_kwargs=dict(color=colors[stage], alpha=0.25))

    # # Indicate saturation point:
    # if stage in ['log', 'inv', 'conv', 'feat']:
    #     ind, dist = crit_inds[stage], crit_dists[stage]
    #     big_axes[2].plot(dist, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
    #                      transform=big_axes[2].get_xaxis_transform()) 
    #     big_axes[2].plot(dist, 0, mfc=colors[stage], mec='k', alpha=0.75, zorder=6, **plateau_dot_kwargs,
    #                      transform=big_axes[2].get_xaxis_transform())
    #     big_axes[2].vlines(dist, big_axes[2].get_ylim()[0], curve[ind],
    #                        color=colors[stage], **plateau_line_kwargs)
del data

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

print('Done.')
embed()