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 reduce_kernel_set, divide_by_zero, 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, xlabel, 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, distances, measures, **kwargs):
    if measures.ndim == 1:
        handles = ax.plot(distances, measures, **kwargs)
        return handles, measures
    median_measure = np.nanmedian(measures, axis=1)
    line_handle = ax.plot(distances, 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 = search_files(song_example, dir='../data/inv/field/song/')[0]
noise_path = search_files(noise_example, dir='../data/inv/field/noise/')[0]
save_path = '../figures/fig_invariance_field.pdf'

# ANALYSIS SETTINGS:
offset_distance = 10 # centimeter
thresh_rel = np.array([0, 0.5, 1, 1.5, 2, 2.5, 3])[4]
kern_subset_kwargs = dict(
    combis=[['measure', 'snip'], ['conv', 'feat']],
    keys=['thresh_abs'],
)

# 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=2,
    wspace=0,
    hspace=0,
    left=0,
    right=1,
    bottom=0,
    top=1,
    height_ratios=[1, 1]
)
subfig_specs = dict(
    snip=(0, slice(None)),
    raw=(1, 0),
    base=(1, 1),
)
snip_grid_kwargs = dict(
    nrows=len(stages),
    ncols=None,
    wspace=0.1,
    hspace=0.4,
    left=0.13,
    right=0.98,
    bottom=0.05,
    top=0.95
)
raw_grid_kwargs = dict(
    nrows=2,
    ncols=1,
    wspace=0,
    hspace=0.15,
    left=0.14,
    right=0.9,
    bottom=0.25,
    top=0.95,
    height_ratios=[0.8, 0.2]
)
base_grid_kwargs = dict(
    nrows=3,
    ncols=1,
    wspace=0,
    hspace=0.25,
    left=raw_grid_kwargs['left'],
    right=raw_grid_kwargs['right'],
    bottom=raw_grid_kwargs['bottom'],
    top=raw_grid_kwargs['top'],
)
inset_dist_bounds = [1.01, 0, 0.95, 1]
inset_ax_bounds = [raw_grid_kwargs['left'], 0.1, raw_grid_kwargs['right'] - raw_grid_kwargs['left'], 0.01]

# PLOT SETTINGS:
fs = dict(
    lab_norm=16,
    lab_tex=20,
    letter=22,
    tit_norm=16,
    tit_tex=20,
    bar=16,
)
stage_colors = load_colors('../data/stage_colors.npz')
kern_colors = dict(
    conv=load_colors('../data/conv_colors_subset.npz'),
    feat=load_colors('../data/feat_colors_subset.npz')
)
lw = dict(
    filt=0.25,
    env=0.25,
    log=0.25,
    inv=0.25,
    conv=0.25,
    feat=1,
    single=3,
    swarm=1,
    legend=5,
    dist=1
)
xlabels = dict(
    high='$1\\,/\\,d\\,\\sim\\,\\alpha$ [cm$^{-1}$]',
    low='distance $d$ [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='$f_i$',
    raw=['$m$', '$\\mu_{f_i}$'],
    base=['$m\\,/\\,m_{\\eta}$', '$\\sigma_{c_i}\\,/\\,\\sigma_{\\eta_i}$', '$\\mu_{f_i}\\,/\\,\\mu_{\\eta_i}$']
)
xlab_high_kwargs = dict(
    y=0.15,
    fontsize=fs['lab_norm'],
    ha='center',
    va='bottom',
)
xlab_low_kwargs = dict(
    y=0,
    fontsize=fs['lab_norm'],
    ha='center',
    va='bottom',
)
ylab_snip_kwargs = dict(
    x=0.03,
    fontsize=fs['lab_tex'],
    rotation=0,
    ha='center',
    va='center',
    ma='center'
)
ylab_big_kwargs = dict(
    x=0,
    fontsize=fs['lab_norm'],
    ha='center',
    va='top',
)
yloc = dict(
    filt=300,
    env=100,
    log=50,
    inv=20,
    conv=0.5,
    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(
    xref=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'${int(1000 * noise_bar_time)}\\,\\text{{ms}}$'
leg_labels = dict(
    filt='$x_{\\text{filt}}$',
    env='$x_{\\text{env}}$',
    log='$x_{\\text{log}}$',
    inv='$x_{\\text{adapt}}$',
    conv='$c_i$',
    feat='$f_i$'
)
leg_kwargs = dict(
    ncols=3,
    loc='upper left',
    bbox_to_anchor=(0.025, 0.9, 0.95, 0.1),
    frameon=False,
    prop=dict(
        size=20,
    ),
    borderpad=0,
    borderaxespad=0,
    handlelength=1,
    columnspacing=1,
    handletextpad=0.5,
    labelspacing=0.1
)
dist_line_kwargs = dict(
    lw=lw['dist'],
)
dist_fill_kwargs = dict(
    lw=lw['dist'],
)

# EXECUTION:

# Load song invariance data:
song_data, config = load_data(song_path, files='distances', keywords=['measure', 'snip', 'thresh'])
t_song = np.arange(song_data['snip_filt'].shape[0]) / config['rate']
dists = song_data['distances'] + offset_distance
scales = 1 / dists
snip_dists = ['noise'] + [f'{int(d)}$\\,$cm' for d in dists]

# Load noise invariance data:
noise_data, _ = load_data(noise_path, keywords=['measure', 'snip', 'thresh'])
noise_data = crop_noise_snippets(noise_data, noise_data['snip_filt'].shape[0], t_song.size)
t_noise = np.arange(noise_data['snip_filt'].shape[0]) / config['rate']

# Reduce kernels:
if reduce_kernels:
    kern_inds = find_kern_specs(config['k_specs'], kernels, types, sigmas)
    config['k_specs'] = config['k_specs'][kern_inds, :]
    config['kernels'] = config['kernels'][:, kern_inds]
    song_data = reduce_kernel_set(song_data, kern_inds, **kern_subset_kwargs)
    noise_data = reduce_kernel_set(noise_data, kern_inds, **kern_subset_kwargs)

# Reduce thresholds:
thresh_ind = np.nonzero(song_data['thresh_rel'] == thresh_rel)[0][0]
song_data['measure_feat'] = song_data['measure_feat'][:, :, thresh_ind]
song_data['snip_feat'] = song_data['snip_feat'][:, :, :, thresh_ind]
noise_data['measure_feat'] = noise_data['measure_feat'][:, :, thresh_ind]
noise_data['snip_feat'] = noise_data['snip_feat'][:, :, :, thresh_ind]

# 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 raw analysis axes:
raw_subfig = fig.add_subfigure(super_grid[subfig_specs['raw']])
raw_grid = raw_subfig.add_gridspec(**raw_grid_kwargs)
raw_axes = np.zeros((raw_grid.nrows,), dtype=object)
for i in range(raw_grid.nrows):
    ax = raw_subfig.add_subplot(raw_grid[i, 0])
    ax.set_xlim(scales[0], scales[-1])
    ax.set_xscale('log')
    ylabel(ax, ylabels['raw'][i], transform=raw_subfig.transSubfigure, **ylab_big_kwargs)
    if i == 0:
        ax.set_yscale('symlog', linthresh=0.00001, linscale=0.1)
        hide_ticks(ax, 'bottom')
    else:
        transform = raw_subfig.transSubfigure + ax.transAxes.inverted()
        inset_x1 = transform.transform((inset_dist_bounds[2], 0))[0]
        inset_dist_bounds[2] = inset_x1 - inset_dist_bounds[0]
        raw_inset = ax.inset_axes(inset_dist_bounds)
        raw_inset.axis('off')
    raw_axes[i] = ax
letter_subplots(raw_axes, 'bc', ref=raw_subfig, **letter_big_kwargs)
xlabel(raw_axes[-1], xlabels['high'], transform=raw_subfig.transSubfigure, **xlab_high_kwargs)

# Prepare base analysis axes:
base_subfig = fig.add_subfigure(super_grid[subfig_specs['base']])
base_grid = base_subfig.add_gridspec(**base_grid_kwargs)
base_axes = np.zeros((base_grid.nrows,), dtype=object)
base_insets = np.zeros((base_grid.nrows - 1,), dtype=object)
for i in range(base_grid.nrows):
    ax = base_subfig.add_subplot(base_grid[i, 0])
    ax.set_xlim(scales[0], scales[-1])
    ax.set_xscale('log')
    ax.set_yscale('log')
    ylabel(ax, ylabels['base'][i], transform=base_subfig.transSubfigure, **ylab_big_kwargs)
    if i < base_grid_kwargs['nrows'] - 1:
        hide_ticks(ax, 'bottom')
    if i > 0:
        inset = ax.inset_axes(inset_dist_bounds)
        inset.set_yscale('log')
        inset.axis('off')
        base_insets[i - 1] = inset
    base_axes[i] = ax
letter_subplots(base_axes, 'def', ref=base_subfig, **letter_big_kwargs)
xlabel(base_axes[-1], xlabels['high'], transform=base_subfig.transSubfigure, **xlab_high_kwargs)

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

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

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

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

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

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

# Plot analysis results:
leg_handles = []
for stage in stages:
    mkey = f'measure_{stage}'
    measure = song_data[mkey]
    color = stage_colors[stage]

    ## UNNORMALIZED MEASURE:

    # Plot single raw intensity curve (median where necessary):
    handles, curve = plot_curves(raw_axes[0], scales, measure, c=color, lw=lw['single'])

    # Add stage-specific proxy legend artist:
    leg_handles.append(raw_axes[0].plot([], [], c=color, label=leg_labels[stage])[0])

    # Plot curve swarm:
    if stage == 'feat':
        # Sync y-limits:
        ylimits(measure, raw_axes[1], minval=0, pad=0.05)
        raw_inset.set_ylim(raw_axes[1].get_ylim())
        # Plot swarm:
        handles = raw_axes[1].plot(scales, measure, lw=lw['swarm'])
        assign_colors(handles, config['k_specs'][:, 0], kern_colors[stage])
        reorder_by_sd(handles, measure)
        # Plot distribution of saturation levels:
        line_kwargs = dist_line_kwargs | dict(c=color)
        fill_kwargs = dist_fill_kwargs | dict(color=color)
        y_dist(raw_inset, measure[-1], nbins=75, log=False,
               line_kwargs=line_kwargs, fill_kwargs=fill_kwargs)

    ## NORMALIZED MEASURE:

    # Relate to noise baseline:
    measure = divide_by_zero(song_data[mkey], noise_data[mkey].mean(axis=0))

    # Plot single baseline-normalized intensity curve (median where necessary):
    handles, curve = plot_curves(base_axes[0], scales, measure, c=color, lw=lw['single'])

    # Plot curve swarm:
    if stage in ['conv', 'feat']:
        i0, i1 = (1, 0) if stage == 'conv' else (2, 1)
        # Sync y-limits:
        ylimits(measure, base_axes[i0], minval=0.9, pad=0.05)
        base_insets[i1].set_ylim(base_axes[i0].get_ylim())
        # Plot swarm:
        handles = base_axes[i0].plot(scales, measure, lw=lw['swarm'])
        assign_colors(handles, config['k_specs'][:, 0], kern_colors[stage])
        reorder_by_sd(handles, measure)
        # Plot distribution of saturation levels:
        line_kwargs = dist_line_kwargs | dict(c=color)
        fill_kwargs = dist_fill_kwargs | dict(color=color)
        y_dist(base_insets[i1], measure[-1], nbins=100, log=True,
               line_kwargs=line_kwargs, fill_kwargs=fill_kwargs)

# Posthoc adjustments:
raw_axes[0].set_ylim(top=100)
base_axes[0].set_ylim(1, 100)
base_axes[1].set_ylim(bottom=1)
base_insets[0].set_ylim(bottom=1)
base_axes[2].set_ylim(bottom=1)
base_insets[1].set_ylim(bottom=1)

# Add secondary x-axes:
for subfig in [raw_subfig, base_subfig]:
    dual_ax = subfig.add_subplot(inset_ax_bounds)
    dual_ax.set_xlim(scales[0], scales[-1])
    dual_ax.set_xscale('log')
    dual_ax.tick_params(axis='x', which='minor', bottom=False)
    dual_ax.tick_params(axis='x', which='major', labelrotation=45)
    dual_ax.set_xticks(scales, dists)
    hide_axis(dual_ax, 'left')
    xlabel(dual_ax, xlabels['low'], transform=subfig.transSubfigure, **xlab_low_kwargs)

# Add legend to first analysis axis:
legend = raw_axes[0].legend(handles=leg_handles, **leg_kwargs)
[handle.set_lw(lw['legend']) for handle in legend.get_lines()]

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

print('Done.')
embed()