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 get_saturation
from color_functions import load_colors
from plot_functions import hide_axis, ylimits, xlabel, ylabel, title_subplot,\
                           plot_line, strip_zeros, time_bar,\
                           letter_subplot, letter_subplots
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)
    for i, ax in enumerate(axes):
        plot_line(ax, time, snippets[:, ..., i], ymin=ymin, ymax=ymax, **kwargs)
    return None

def plot_curves(ax, scales, measures, fill_kwargs={}, **kwargs):
    if measures.ndim == 1:
        ax.plot(scales, measures, **kwargs)[0]
        return measures
    median_measure = np.median(measures, axis=1)
    spread_measure = [np.percentile(measures, 25, axis=1),
                      np.percentile(measures, 75, axis=1)]
    ax.plot(scales, median_measure, **kwargs)[0]
    ax.fill_between(scales, *spread_measure, **fill_kwargs)
    return median_measure

def show_saturation(ax, scales, measures, high=0.95, **kwargs):
    high_ind = get_saturation(measures, high=high)[1]
    return ax.plot(scales[high_ind], 0, transform=ax.get_xaxis_transform(),
                   marker='o', ms=10, zorder=6, clip_on=False, **kwargs)

# GENERAL SETTINGS:
target_species = [
    'Chorthippus_biguttulus',
    'Chorthippus_mollis',
    'Chrysochraon_dispar',
    'Euchorthippus_declivus',
    'Gomphocerippus_rufus',
    'Omocestus_rufipes',
    'Pseudochorthippus_parallelus',
][5]
example_file = {
    'Chorthippus_biguttulus': 'Chorthippus_biguttulus_GBC_94-17s73.1ms-19s977ms',
    'Chorthippus_mollis': 'Chorthippus_mollis_DJN_41_T28C-46s4.58ms-1m15s697ms',
    'Chrysochraon_dispar': 'Chrysochraon_dispar_DJN_26_T28C_DT-32s134ms-34s432ms',
    'Euchorthippus_declivus': 'Euchorthippus_declivus_FTN_79-2s167ms-2s563ms',
    'Gomphocerippus_rufus': 'Gomphocerippus_rufus_FTN_91-3-884ms-10s427ms',
    'Omocestus_rufipes': 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms',
    'Pseudochorthippus_parallelus': 'Pseudochorthippus_parallelus_GBC_88-6s678ms-9s32.3ms'
}[target_species]
raw_path = search_files(target_species, incl='raw', dir='../data/inv/full/condensed/')[0]
norm_path = search_files(target_species, incl='norm', dir='../data/inv/full/condensed/')[0]
snip_path = search_files(example_file, dir='../data/inv/full/')[0]
trace_path = search_files(target_species, dir='../data/inv/full/collected/')[0]
ref_path = '../data/inv/full/ref_measures.npz'
save_path = '../figures/fig_invariance_full.pdf'
stages = ['filt', 'env', 'log', 'inv', 'conv', 'feat']
load_kwargs = dict(
    files=stages,
    keywords=['scales', 'snip', 'measure']
)

# GRAPH SETTINGS:
fig_kwargs = dict(
    figsize=(32/2.54, 20/2.54),
)
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=0.08,
    right=0.95,
    bottom=0.08,
    top=0.95
)
big_grid_kwargs = dict(
    nrows=1,
    ncols=3,
    wspace=0.2,
    hspace=0,
    left=snip_grid_kwargs['left'],
    right=0.96,
    bottom=0.2,
    top=0.95
)

# 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')
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='scale $\\alpha$',
)
ylabels = dict(
    filt='$x_{\\text{filt}}$',
    env='$x_{\\text{env}}$',
    log='$x_{\\text{db}}$',
    inv='$x_{\\text{inv}}$',
    conv='$c_i$',
    feat='$f_i$',
    big=['intensity', 'rel. intensity', 'norm. intensity']
)
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=-0.12,
    fontsize=fs['lab_norm'],
    ha='center',
    va='bottom',
)
yloc = dict(
    filt=3000,
    env=1000,
    log=50,
    inv=20,
    conv=2,
    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'],
)
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',
    )
)
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 invariance data:
raw_data, config = load_data(raw_path, files='scales', keywords='mean')
norm_data, _ = load_data(norm_path, files='scales', keywords='mean')
scales = raw_data['scales']

# Load snippet data:
snip, _ = load_data(snip_path, files='example_scales', keywords='snip')
t_full = np.arange(snip['snip_filt'].shape[0]) / config['rate']
snip_scales = snip['example_scales']

# Adjust grid parameters:
snip_grid_kwargs['ncols'] = snip_scales.size

# 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.set_xlim(t_full[0], t_full[-1])
    ax.yaxis.set_major_locator(plt.MultipleLocator(yloc[stages[i]]))
    hide_axis(ax, 'bottom')
    if i == 0:
        title = title_subplot(ax, f'$\\alpha={strip_zeros(snip_scales[j])}$',
                              ref=snip_subfig, **title_kwargs)
    if j == 0:
        ylabel(ax, ylabels[stages[i]], **ylab_snip_kwargs, transform=snip_subfig.transSubfigure)
    else:
        hide_axis(ax, 'left')
    snip_axes[i, j] = ax
time_bar(snip_axes[-1, -1], **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(scales[0], scales[-1])
    ax.set_xscale('symlog', linthresh=scales[1], linscale=0.5)
    ax.set_yscale('symlog', linthresh=0.01, linscale=0.1)
    xlabel(ax, xlabels['big'], transform=big_subfig, **xlab_big_kwargs)
    ylabel(ax, ylabels['big'][i], **ylab_big_kwargs)
    big_axes[i] = ax
letter_subplots(big_axes, 'bc', **letter_big_kwargs)

if False:
    # Plot filtered snippets:
    plot_snippets(snip_axes[0, :], t_full, snip['snip_filt'],
                c=colors['filt'], lw=lw['filt'])

    # Plot envelope snippets:
    plot_snippets(snip_axes[1, :], t_full, snip['snip_env'],
                ymin=0, c=colors['env'], lw=lw['env'])

    # Plot logarithmic snippets:
    plot_snippets(snip_axes[2, :], t_full, snip['snip_log'],
                c=colors['log'], lw=lw['log'])

    # Plot invariant snippets:
    plot_snippets(snip_axes[3, :], t_full, snip['snip_inv'],
                c=colors['inv'], lw=lw['inv'])

    # Plot kernel response snippets:
    plot_snippets(snip_axes[4, :], t_full, snip['snip_conv'],
                c=colors['conv'], lw=lw['conv'])

    # Plot feature snippets:
    plot_snippets(snip_axes[5, :], t_full, snip['snip_feat'],
                ymin=0, ymax=1, c=colors['feat'], lw=lw['feat'])

# Plot analysis results:
for stage in stages:
    # Get average unnormed measure across recordings:
    raw_measure = raw_data[f'mean_{stage}'].mean(axis=-1)

    # Plot unmodified intensity measures:
    curve = plot_curves(big_axes[0], scales, raw_measure, 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]
        scale = scales[ind]
        big_axes[0].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
                         transform=big_axes[0].get_xaxis_transform()) 
        big_axes[0].plot(scale, 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(scale, big_axes[0].get_ylim()[0], curve[ind],
                           color=colors[stage], **plateau_line_kwargs)

    # Get average noise-related measure across recordings:
    norm_measure = norm_data[f'mean_{stage}'].mean(axis=-1)

    # Plot noise-related intensity measure:
    curve = plot_curves(big_axes[1], scales, norm_measure, 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']:
        big_axes[1].plot(scale, 0, c='w', alpha=1, zorder=5.5, **plateau_dot_kwargs,
                         transform=big_axes[1].get_xaxis_transform()) 
        big_axes[1].plot(scale, 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(scale, big_axes[1].get_ylim()[0], curve[ind],
                           color=colors[stage], **plateau_line_kwargs)

    # Normalize measure to [0, 1]:
    min_measure = raw_measure.min(axis=0)
    max_measure = raw_measure.max(axis=0)
    norm_measure = (raw_measure - min_measure) / (max_measure - min_measure)

    # Plot range-normalized intensity measure:
    curve = plot_curves(big_axes[2], scales, norm_measure, 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']:
        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=colors[stage], 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], curve[ind],
                           color=colors[stage], **plateau_line_kwargs)

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

print('Done.')
embed()