"""
Build layout for histogram plots
"""

__all__ = ['var_plot_size', 'plot_vars', 'plot_var']

from math import ceil, sqrt

import numpy as np
from matplotlib import pyplot as plt

# Set space between plots in horiz and vert.
H_SPACE = 0.2
V_SPACE = 0.2

# Set top, bottom, left margins.
T_MARGIN = 0.2
B_MARGIN = 0.2
L_MARGIN = 0.2
R_MARGIN = 0.4

# Set desired plot sizes.
TILE_W = 3.0
TILE_H = 2.0
CBAR_WIDTH = 0.75


def var_plot_size(n):
    ncol, nrow = tile_axes_square(n)

    # Calculate total width and figure size
    plots_width = (TILE_W+H_SPACE)*ncol
    figwidth = plots_width + CBAR_WIDTH + L_MARGIN + R_MARGIN
    figheight = (TILE_H+V_SPACE)*nrow + T_MARGIN + B_MARGIN
    return figwidth, figheight

def _make_var_axes(n):
    """
    Build a figure with one axis per parameter,
    and one axis (the last one) to contain the colorbar.
    Use to make the vars histogram figure.
    """
    fig = plt.gcf()
    fig.clf()
    total_width, total_height = fig.get_size_inches()

    ncol, nrow = tile_axes_square(n)

    # Calculate dimensions as a faction of figure size.
    v_space_f = V_SPACE/total_height
    h_space_f = H_SPACE/total_width
    t_margin_f = T_MARGIN/total_height
    b_margin_f = B_MARGIN/total_height
    l_margin_f = L_MARGIN/total_width
    top = 1 - t_margin_f+v_space_f
    left = l_margin_f

    tile_h = (total_height - T_MARGIN - B_MARGIN)/nrow - V_SPACE
    tile_w = (total_width - L_MARGIN - R_MARGIN - CBAR_WIDTH)/ncol - H_SPACE
    tile_h_f = tile_h/total_height
    tile_w_f = tile_w/total_width

    # Calculate colorbar location (left, bottom) and colorbar height.
    l_cbar_f = l_margin_f + ncol*(tile_w_f+h_space_f)
    b_cbar_f = b_margin_f + v_space_f
    cbar_w_f = CBAR_WIDTH/total_width
    cbar_h_f = 1 - t_margin_f - b_margin_f - v_space_f
    cbar_box = [l_cbar_f, b_cbar_f, cbar_w_f, cbar_h_f]

    k = 0
    for j in range(1, nrow+1):
        for i in range(0, ncol):
            if k >= n:
                break
            dims = [left + i*(tile_w_f+h_space_f),
                    top - j*(tile_h_f+v_space_f),
                    tile_w_f,
                    tile_h_f]
            ax = fig.add_axes(dims)
            ax.set_facecolor('none')
            k += 1

    fig.add_axes(cbar_box)
    #fig.set_size_inches(total_width, total_height)
    return fig


def tile_axes_square(n):
    """
    Determine number of columns by finding the
    next greatest square, then determine number
    of rows needed.
    """
    cols = int(ceil(sqrt(n)))
    rows = int(ceil(n/float(cols)))
    return cols, rows


def plot_vars(draw, all_vstats, **kw):
    n = len(all_vstats)
    fig = _make_var_axes(n)
    cbar = _make_fig_colorbar(draw.logp)
    for k, vstats in enumerate(all_vstats):
        fig.sca(fig.axes[k])
        plot_var(draw, vstats, k, cbar, **kw)


def plot_var(draw, vstats, var, cbar, nbins=30):
    values = draw.points[:, var].flatten()
    bin_range = vstats.p95_range
    #bin_range = np.min(values), np.max(values)
    _make_logp_histogram(values, draw.logp, nbins, bin_range,
                         draw.weights, cbar)
    _decorate_histogram(vstats)


def _decorate_histogram(vstats):
    import pylab
    from matplotlib.transforms import blended_transform_factory as blend

    l95, h95 = vstats.p95_range
    l68, h68 = vstats.p68_range

    # Shade things inside 1-sigma
    pylab.axvspan(l68, h68, color='gold', alpha=0.5, zorder=-1)
    # build transform with x=data, y=axes(0,1)
    ax = pylab.gca()
    transform = blend(ax.transData, ax.transAxes)

    def marker(symbol, position):
        if position < l95:
            symbol, position, ha = '<'+symbol, l95, 'left'
        elif position > h95:
            symbol, position, ha = '>'+symbol, h95, 'right'
        else:
            symbol, position, ha = symbol, position, 'center'
        pylab.text(position, 0.95, symbol, va='top', ha=ha,
                   transform=transform, zorder=3, color='g')
        #pylab.axvline(v)

    marker('|', vstats.median)
    marker('E', vstats.mean)
    marker('*', vstats.best)

    pylab.text(0.01, 0.95, vstats.label, zorder=2,
               backgroundcolor=(1, 1, 0, 0.2),
               verticalalignment='top',
               horizontalalignment='left',
               transform=pylab.gca().transAxes)
    ax.set_yticklabels([])


def _make_fig_colorbar(logp):
    import matplotlib as mpl
    import pylab

    # Option 1: min to min + 4
    #vmin=-max(logp); vmax=vmin+4
    # Option 1b: min to min log10(num samples)
    #vmin=-max(logp); vmax=vmin+log10(len(logp))
    # Option 2: full range of best 98%
    snllf = pylab.sort(-logp)
    vmin, vmax = snllf[0], snllf[int(0.98*(len(snllf)-1))]  # robust range
    # Option 3: full range
    #vmin,vmax = -max(logp),-min(logp)

    fig = pylab.gcf()
    ax = fig.axes[-1]
    cmap = mpl.cm.copper

    # Set the colormap and norm to correspond to the data for which
    # the colorbar will be used.
    norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)

    # ColorbarBase derives from ScalarMappable and puts a colorbar
    # in a specified axes, so it has everything needed for a
    # standalone colorbar.  There are many more kwargs, but the
    # following gives a basic continuous colorbar with ticks
    # and labels.
    class MinDigitsFormatter(mpl.ticker.Formatter):
        def __init__(self, low, high):
            self.delta = high - low

        def __call__(self, x, pos=None):
            return format_value(x, self.delta)

    ticks = ()  #(vmin, vmax)
    formatter = MinDigitsFormatter(vmin, vmax)
    cbar = mpl.colorbar.ColorbarBase(
        ax, cmap=cmap, norm=norm,
        ticks=ticks, format=formatter,
        orientation='vertical')
    #cb.set_ticks(ticks)
    #cb.set_ticklabels(labels)
    #cb.set_label('negative log likelihood')

    cbar_box = ax.get_position().bounds
    fig.text(cbar_box[0],cbar_box[1],
             '{:.3G}'.format(vmin), va='top')
    fig.text(cbar_box[0], cbar_box[1]+cbar_box[3],
             '{:.3G}'.format(vmax), va='bottom')

    return cbar


def _make_logp_histogram(values, logp, nbins, ci, weights, cbar):
    from numpy import (ones_like, searchsorted, linspace, cumsum, diff,
                       unique, argsort, array, hstack, exp)
    if weights is None:
        weights = ones_like(logp)
    # TODO: values are being sorted to collect stats and again to plot
    idx = argsort(values)
    values, weights, logp = values[idx], weights[idx], logp[idx]
    #open('/tmp/out','a').write("ci=%s, range=%s\n"
    #                           % (ci,(min(values),max(values))))
    edges = linspace(ci[0], ci[1], nbins+1)
    idx = searchsorted(values[1:-1], edges)
    #weightsum = cumsum(weights)
    #heights = diff(weightsum[idx])/weightsum[-1]  # normalized weights

    import pylab
    edgecolors = None
    cmap = cbar.cmap
    cmap_edges = linspace(0, 1, cmap.N+1)[1:-1]
    bins = []  # marginalized maximum likelihood
    for s, e, xlo, xhi \
            in zip(idx[:-1], idx[1:], edges[:-1], edges[1:]):
        if s == e:
            continue
        # parameter interval endpoints
        x = array([xlo, xhi], 'd')
        # -logp values within interval, with sort index from low to high
        pv = -logp[s:e]
        pidx = argsort(pv)
        pv = pv[pidx]
        # weights for samples within interval, sorted
        pw = weights[s:e][pidx]
        # vertical colorbar top edges is the cumulative sum of the weights
        y_top = cumsum(pw)

        # For debugging compare with one rectangle per sample
        if False:
            import matplotlib as mpl
            cmap = mpl.cm.flag
            edgecolors = 'k'
            xmid = (xlo+xhi)/2
            x = [xlo, xmid]
            y = hstack((0, y_top))
            z = pv[:, None]
            pylab.pcolormesh(x, y, z, norm=cbar.norm, cmap=cmap)
            x = [xmid, xhi]

        # Possibly millions of samples, so group those which have the
        # same colour instead of drawing each as its own rectangle.
        #
        # Norm the values then look up the colormap edges in the sorted
        # normed negative log probabilities.  Drop duplicates, which
        # represent zero-width bars. Assign the value for each interval
        # according to the value at the change point.
        #
        # The indexing logic is very ugly. The searchsorted() function
        # returns 0 if before the first or N if after the last, so the
        # end points of the range are dropped so that there is and implicit
        # [-inf, ... interior points ..., inf] range. Similarly, colours
        # below vmin go to vmin and above vmax go to vmax, so drop those
        # end points as well. Then put the end points back on in the
        # found indices [0, ... interior edges ..., N-1]. Use the value
        # at the end of the boundary to colour the section.
        # Something is not quite right: with this algorithm the first
        # block appears to always be one element long and is often the
        # same colour as the next block. This is only visible if edges
        # are drawn so ignore it for now.
        change_point = searchsorted(cbar.norm(pv[1:-1]), cmap_edges)
        tops = unique(hstack((change_point, len(pv)-1)))
        y = hstack((0, y_top[tops]))
        z = pv[tops][:, None]
        pylab.pcolormesh(
            x, y, z, norm=cbar.norm, cmap=cmap, edgecolors=edgecolors)

        # centerpoint, histogram height, maximum likelihood for each bin
        bins.append(((xlo+xhi)/2, y_top[-1], exp(cbar.norm.vmin-pv[0])))
    # Check for broken distribution
    if not bins:
        return
    centers, height, maxlikelihood = array(bins).T
    # Normalize maximum likelihood plot so it contains the same area as the
    # histogram, unless it is really spikey, in which case make sure it has
    # about the same height as the histogram.
    maxlikelihood *= np.sum(height)/np.sum(maxlikelihood)
    hist_peak = np.max(height)
    ml_peak = np.max(maxlikelihood)
    if ml_peak > hist_peak*1.3:
        maxlikelihood *= hist_peak*1.3/ml_peak
    pylab.plot(centers, maxlikelihood, '-g')

    ## plot marginal gaussian approximation along with histogram
    #def G(x, mean, std):
    #    return np.exp(-((x-mean)/std)**2/2)/np.sqrt(2*np.pi*std**2)
    ## TODO: use weighted average for standard deviation
    #mean, std = np.average(values, weights=weights), np.std(values, ddof=1)
    #pdf = G(centers, mean, std)
    #pylab.plot(centers, pdf*np.sum(height)/np.sum(pdf), '-b')


def _make_var_histogram(values, logp, nbins, ci, weights):
    # Produce a histogram
    hist, bins = np.histogram(values, bins=nbins, range=ci,
                              #new=True,
                              density=True, weights=weights)

    # Find the max likelihood for values in each bin
    edges = np.searchsorted(values, bins)
    histbest = [np.max(logp[edges[i]:edges[i+1]])
                if edges[i] < edges[i+1] else -np.inf
                for i in range(nbins)]

    # scale to marginalized probability with peak the same height as hist
    histbest = np.exp(np.asarray(histbest) - max(logp)) * np.max(hist)

    import pylab
    # Plot the histogram
    pylab.bar(bins[:-1], hist, width=bins[1]-bins[0])

    # Plot the kernel density estimate
    #density = KDE1D(values)
    #x = linspace(bins[0],bins[-1],100)
    #pylab.plot(x, density(x), '-k')

    # Plot the marginal maximum likelihood
    centers = (bins[:-1]+bins[1:])/2
    pylab.plot(centers, histbest, '-g')