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
from color_functions import load_colors
from plot_functions import hide_axis, ylimits, super_xlabel, ylabel, title_subplot,\
                           plot_line, strip_zeros, time_bar, assign_colors,\
                           letter_subplot, letter_subplots, reorder_by_sd
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, fill_kwargs={}, **kwargs):
    if measures.ndim == 1:
        ax.plot(scales, measures, **kwargs)[0]
        return measures
    median_measure = np.nanmedian(measures, axis=1)
    spread_measure = [np.nanpercentile(measures, 25, axis=1),
                      np.nanpercentile(measures, 75, axis=1)]
    ax.plot(scales, median_measure, **kwargs)[0]
    ax.fill_between(scales, *spread_measure, **fill_kwargs)
    return median_measure

def exclude_zero_scale(data, stages):
    inds = data['scales'] > 0
    data['scales'] = data['scales'][inds]
    for stage in stages:
        data[f'mean_{stage}'] = data[f'mean_{stage}'][inds, ...]
    return data

def reduce_kernel_set(data, inds, keyword, stages=['conv', 'feat']):
    for stage in stages:
        key = f'{keyword}_{stage}'
        data[key] = data[key][:, inds, ...]
    return data


# 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]
stages = ['filt', 'env', 'inv', 'conv', 'feat']
raw_path = search_files(target_species, incl='unnormed', dir='../data/inv/short/condensed/')[0]
base_path = search_files(target_species, incl='base', dir='../data/inv/short/condensed/')[0]
range_path = search_files(target_species, incl='range', dir='../data/inv/short/condensed/')[0]
snip_path = search_files(example_file, dir='../data/inv/short/')[0]
save_path = '../figures/fig_invariance_short.pdf'

# ANALYSIS SETTINGS:
exclude_zero = True

# SUBSET SETTINGS:
types = np.array([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, 0.032])
# types = [1, -1, 2, -2, 3, -3, 4, -4, 5, -5, 6, -6, 7, -7, 8, -8, 9, -9, 10, -10]
# sigmas = [0.001, 0.002, 0.004, 0.008, 0.016, 0.032]
kernels = np.array([
    [1, 0.002],
    [-1, 0.002],
    [2, 0.004],
    [-2, 0.004],
    [3, 0.032],
    [-3, 0.032]
])
kernels = None

# 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,
    conv=0.25,
    inv=0.25,
    feat=1,
    big=3,
    plateau=1.5,
)
xlabels = dict(
    big='scale $\\alpha$',
)
ylabels = dict(
    filt='$x_{\\text{filt}}$',
    env='$x_{\\text{env}}$',
    inv='$x_{\\text{adapt}}$',
    conv='$c_i$',
    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=3000,
    env=1000,
    inv=1000,
    conv=30,
    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 raw (unnormed) invariance data:
data, config = load_data(raw_path, files='scales', keywords='mean')
if exclude_zero:
    data = exclude_zero_scale(data, stages)
scales = 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']

# 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')
    snip = reduce_kernel_set(snip, kern_inds, keyword='snip')
    reduce_kernels = True

# 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)
    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, :], 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 "adapted" snippets:
    plot_snippets(snip_axes[2, :], t_full, snip['snip_inv'],
                  c=colors['inv'], lw=lw['inv'])

    # Plot kernel response snippets:
    all_handles = plot_snippets(snip_axes[3, :], t_full, 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, snip['snip_conv'][..., i])

    # Plot feature snippets:
    all_handles = plot_snippets(snip_axes[4, :], t_full, 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, snip['snip_feat'][..., i])
del snip

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

# Unnormed measures:
for stage in stages:
    # Plot average intensity measure across recordings:
    curve = plot_curves(big_axes[0], scales, data[f'mean_{stage}'].mean(axis=-1),
                        c=colors[stage], lw=lw['big'],
                        fill_kwargs=dict(color=colors[stage], alpha=0.25))
    # Indicate saturation point:
    if stage == '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)
        # Log saturation point:
        crit_inds[stage] = ind
        crit_scales[stage] = scale
del data

# Noise baseline-related measures:
data, _ = load_data(base_path, files='scales', keywords='mean')
if exclude_zero:
    data = exclude_zero_scale(data, stages)
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], scales, data[f'mean_{stage}'].mean(axis=-1),
                        c=colors[stage], lw=lw['big'],
                        fill_kwargs=dict(color=colors[stage], alpha=0.25))
    # Indicate saturation point:
    if stage == 'feat':
        ind, scale = crit_inds[stage], crit_scales[stage]
        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)
del data

# Min-max normalized measures:
data, _ = load_data(range_path, files='scales', keywords='mean')
if exclude_zero:
    data = exclude_zero_scale(data, stages)
if reduce_kernels:
    data = reduce_kernel_set(data, kern_inds, keyword='mean')
for stage in ['feat']:
    # Plot average intensity measure across recordings:
    curve = plot_curves(big_axes[2], scales, data[f'mean_{stage}'].mean(axis=-1),
                        c=colors[stage], lw=lw['big'],
                        fill_kwargs=dict(color=colors[stage], alpha=0.25))

    # Indicate saturation point:
    if stage == 'feat':
        ind, scale = crit_inds[stage], crit_scales[stage]
        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)
del data

# Save graph:
if save_path is not None:
    file_name = save_path.replace('.pdf', f'_{target_species}.pdf')
    fig.savefig(file_name)
plt.show()

print('Done.')
embed()